The integration of advanced driver assistance systems (ADAS) and the transition towards electric vehicles (EVs) are significantly transforming the automotive industry.

Modern vehicles, essentially computers on wheels, require substantially more semiconductors. In response, carmakers are forming stronger partnerships with semiconductor vendors – some are taking a page from tech giants like Apple and Samsung by designing their own chips, often following a fabless or outsourced production model.

While a deeper connection with semiconductor design helps automakers maintain design control and supply chain resilience, it also imposes substantial responsibility to understand and meet stringent automotive quality standards.

The crucial role of semiconductor testing

Testing is vital to meet the automotive industry’s demands for quality, cost-efficiency, and timely market entry. As carmakers delve into semiconductor design, they face new challenges. Advanced semiconductors, more complex by nature, require thorough testing to ensure automotive-grade quality.

The industry’s push towards smaller process nodes, like 5nm and below, further amplifies these challenges, necessitating early and continuous engagement with testing resources to maintain high standards without compromising time to market.

Zero defects commitment

The automotive industry’s commitment to zero defects underscores the critical importance of quality. This commitment is based on an analysis of the costs associated with testing versus the potentially catastrophic costs of failures, such as life-threatening malfunctions, costly recalls, and market delays.

These issues can dramatically impact revenue and market position, highlighting the need for rigorous testing. The exceptional quality requirements inherent to automotive standards are set to intensify with the increasing digital complexity of vehicles.

Given that automotive chips must perform reliably over a lifespan of 10 to 20 years, comprehensive testing protocols play an essential role in identifying and rectifying defects early, optimizing both cost and quality. This fundamental aspect of semiconductor manufacturing cements the principle that quality is not just a priority, but the paramount concern.

This commitment transcends the capabilities of even the most skilled engineers, requiring systematic and integrated testing processes to ensure chip reliability and performance under diverse conditions.

Collaboration is key

Collaboration between automakers and semiconductor manufacturers is crucial, fostering an environment where issues can be identified and addressed early in the development cycle.

These partnerships are vital for maintaining momentum in the face of rapid technological advancements and ensuring that the automotive industry can meet the high standards of safety, reliability, and performance expected by consumers.

This collaborative approach helps to optimize testing processes, to maintain stringent quality standards, and to protect time-to-market goals, preventing production delays and ensuring the continuous advancement of automotive technologies.

The post Semiconductor Shifts In Automotive: Impact Of EV And ADAS Trends appeared first on Semiconductor Engineering.

Gallium nitride is starting to make broader inroads in the lower-end of the high-voltage, wide-bandgap power FET market, where silicon carbide has been the technology of choice. This shift is driven by lower costs and processes that are more compatible with bulk silicon.

Efficiency, power density (size), and cost are the three major concerns in power electronics, and GaN can meet all three criteria. However, to satisfy all of those criteria consistently, the semiconductor ecosystem needs to develop best practices for test, inspection, and metrology, determining what works best for which applications and under varying conditions.

Power ICs play an essential role in stepping up and down voltage levels from one power source to another. GaN is used extensively today in smart phone and laptop adapters, but market opportunities are beginning to widen for this technology. GaN likely will play a significant role in both data centers and automotive applications [1]. Data centers are expanding rapidly due to the focus on AI and a build-out at the edge. And automotive is keen to use GaN power ICs for inverter modules because they will be cheaper than SiC, as well as for onboard battery chargers (OBCs) and various DC-DC conversions from the battery to different applications in the vehicle.

Fig. 1: Current and future fields of interest for GaN and SiC power devices. Source A. Meixner/Semiconductor Engineering

But to enter new markets, GaN device manufactures need to more quickly ramp up new processes and their associated products. Because GaN for power transistors is a developing process technology, measurement data is critical to qualify both the manufacturing process and the reliability of the new semiconductor technology and resulting product.

Much of GaN’s success will depend on metrology and inspection solutions that offer high throughput, as well as non-destructive testing methods such as optical and X-ray. Electron microscopy is useful for drilling down into key device parameters and defect mechanisms. And electrical tests provide complementary data that assists with product/process validation, reliability and qualification, system-level validation, as well as being used for production screening.

Silicon carbide (SiC) remains the material of choice for very high-voltage applications. It offers better performance and higher efficiency than silicon. But SiC is expensive. It requires different equipment than silicon, it’s difficult to grow SiC ingots, and today there is limited wafer capacity.

In contrast, GaN offers some of the same desirable characteristics as SiC and can operate at even higher switching speeds. GaN wafer production is cheaper because it can be created on a silicon substrate utilizing typical silicon processing equipment other than the GaN epitaxial deposition tool. That enables a fab/foundry with a silicon CMOS process to ramp a GaN process with an engineering team experienced in GaN.

The cost comparison isn’t entirely apples-to-apples, of course. The highest-voltage GaN on the market today uses silicon on sapphire (SoS) or other engineered substrates, which are more expensive. But below those voltages, GaN typically has a cost advantage, and that has sparked renewed interest in this technology.

“GaN-based products increase the performance envelopes relative to the incumbent and mature silicon-based technologies,” said Vineet Pancholi, senior director of test technology at Amkor. “Switching speeds with GaN enable the application in ways never possible with silicon. But as the GaN production volumes ramp, these products have extreme economic pressures. The production test list includes static attributes. However, the transient and dynamic attributes are the primary benefit of GaN in the end application.”

Others agree. “The world needs cheaper material, and GaN is easy to build,” said Frank Heidemann, vice president and technology leader of SET at NI/Emerson Test & Measurement. “Gallium nitride has a huge success in the lower voltages ranges — anything up to 500V. This is where the GaN process is very well under control. The problem now is building in higher voltages is a challenge. In the near future there will be products at even higher voltage levels.”

Those higher-voltage applications require new process recipes, new power IC designs, and subsequently product/process validation and qualification.

GaN HEMT properties
Improving the processes needed to create GaN high-electron-mobility transistors (HEMTs) requires a deep understanding of the material properties and the manufacturing consequences of layering these materials.

The underlying physics and structure of wide-bandgap devices significantly differs from silicon high-voltage transistors. Silicon transistors rely on doping of p and n materials. When voltage is applied at the gate, it creates a channel for current to flow from source to drain. In contrast, wide-bandgap transistors are built by layering thin films of different materials, which differ in their bandgap energy. [2] Applying a voltage to the gate enables an electron exchange between the two materials, driving those electrons along the channel between source and drain.

Fig. 2. Cross-sectional animation of e-mode GaN HEMT device. Source: Zeiss Microscopy

“GaN devices rely on two-dimensional electron gas (2DEG) created at the GaN and AlGaN interface to conduct current at high speed,” said Jiangtao Hu, senior director of product marketing at Onto Innovation. “To enable high electron mobility, the epitaxy process creating complex multi-layer crystalline films must be carefully monitored and controlled, ensuring critical film properties such as thickness, composition, and interface roughness are within a tight spec. The ongoing trend of expanding wafer sizes further requires the measurement to be on-product and non-destructive for uniformity control.”

Fig. 3: SEM cross-section of enhancement-mode GaN HEMT built on silicon which requires a superlattice. Source: Zeiss Microscopy

Furthermore, each layer’s electrical properties need to be understood. “It is of utmost importance to determine, as early as possible in the manufacturing process, the electrical characteristics of the structures, the sheet resistance of the 2DEG, the carrier concentration, and the mobility of carriers in the channel, preferably at the wafer level in a non-destructive assessment,” said Christophe Maleville, CTO and senior executive vice president of innovation at Soitec.

Developing process recipes for GaN HEMT devices at higher operating ranges require measurements taken during wafer manufacturing and device testing, both for qualification of a process/product and production manufacturing. Inspection, metrology, and electrical tests focus on process anomalies and defects, which impact the device performance.

“Crystal defects such as dislocations and stacking faults, which can form during deposition and subsequently be grown over and buried, can create long-term reliability concerns even if the devices pass initial testing,” said David Taraci, business development manager of electronics strategic accounts at ZEISS Research Microscopy Solutions. “Gate oxides can pinch off during deposition, creating voids which may not manifest as an issue immediately.”

The quality of the buffer layer is critical because it affects the breakdown voltage. “The maximum breakdown voltage of the devices will be ultimately limited by the breakdown of the buffer layer grown in between the Si substrate and the GaN channel,” said Soitec’s Maleville. “An electrical assessment (IV at high voltage) requires destructive measurements as well as device isolation. This is performed on a sample basis only.”

One way to raise the voltage limit of a GaN device is to add a ‘gate driver’ which keeps it reliable at higher voltages. But to further expand GaN technology’s performance envelope to higher voltage operation engineers need to comprehend a new GaN device reliability properties.

“We are supporting GaN lifetime validation, which is the prediction of a mission characteristic of lifetime for gallium nitride power devices,” said Emerson’s Heidemann. “Engineers build physics-based failure models of these devices. Next, they investigate the acceleration factors. How can we really make tests and verification properly so that we can assess lifetime health?”

The qualification procedures necessitate life-stressing testing, which duplicates predicated mission profile usage, as well as electrical testing, after each life-stress period. That allows engineers to determine shifts in transistor characteristics and outright failures. For example, life stress periods could start with 4,000 hours and increase in 1,000-hour increments to 12,000 hours, during which time the device is turned on/off with specific durations of ‘on’ times.

“Reliability predictions are based upon application mission profiles,” said Stephanie Watts Butler, independent consultant and vice president of industry and standards in the IEEE Power Electronics Society. “In some cases, GaN is going into a new application, or being used differently than silicon, and the mission profile needs to be elucidated. This is one area that the industry is focused upon together.”

As an example of this effort, Butler pointed to JEDEC JEP186 spec [3], which provides guidelines for specifying the breakdown voltage for GaN HEMT devices. “JEDEC and IEC both are issuing guideline documents for methods for test and characterization of wide-bandgap devices, as well as reliability and qualification procedures, and datasheet parameters to enable wide bandgap devices, including GaN, to ramp faster with higher quality in the marketplace,” she said.

Electrical tests remain essential to screening for both time-zero and reliability-associated defects (e.g. infant mortality and reduced lifetime). This holds true for screening wafers, singulated die, and packaged devices. And test content includes tests specific to GaN HEMT power devices performance specifications and tests more directed at defect detection.

Due to inherent device differences, the GaN test list varies in some significant ways from Si and SiC power ICs. Assessing GaN health for qualification and manufacturing purposes requires both static and dynamic tests (SiC DC and AC). A partial list includes zero gate voltage drain leakage current, rise time, fall time, dynamic RDS_on, and dielectric integrity tests.

“These are very time-intensive measurement techniques for GaN devices,” said Tom Tran, product manager for power discrete test products at Teradyne. “On top of the static measurement techniques is the concern about trapped charge — both for functionality and efficiency — revealed through dynamic RDS_on testing.”

Transient tests are necessary for qualification and production purposes due to the high electron mobility, which is what gives GaN HEMT its high switching speed. “From a test standpoint, static test failures indicate basic processing failures, while transient switching failures indicate marginal or process excursions,” said Amkor’s Vineet Pancholi. “Both tests continue to be important to our customers until process maturity is achieved. With the extended range of voltage, current, and switching operations, mainstream test equipment suppliers have been adding complementary instrumentation capabilities.”

And ATE suppliers look to reduce test time, which reduces cost. “Both static and dynamic test requirements drive very high test times,” said Teradyne’s Tran. “But the GaN of today is very different than GaN from a decade ago. We’re able to accelerate this testing just due to the core nature of our ATE architecture. We think there is the possibility further reducing the cost of test for our customers.”

Tools for process control and quality management
GaN HEMT devices’ reliance on thin-film processes highlights the need to understand the material properties and the nature of the interfaces between each layer. That requires tools for process control, yield management, and failure analysis.

“GaN device performance is highly reflective of the film characteristics used in its manufacture,” said Mike McIntyre, director of software product management at Onto Innovation. “The smallest process variations when it comes to film thickness, film stress, line width or even crystalline make-up, can have a dramatic impact on how the device performs, or even if it is usable in its target market. This lack of tolerance to any variation places a greater burden on engineers to understand the factors that correlate to device performance and its profitability.”

Inspection methods that are non-destructive vary in throughput time and in the level of detail provided for engineers to make decisions. While optical methods are fast and provide full wafer coverage, they cannot accurately classify chemical or structural defects for engineers/technicians to review. In contrast, destructive methods provide the information that’s needed to truly understand the nature of the defects. For example, conductive atomic force microscopy (AFM) probing remains slow, but it can identify electrical nature of a defect. And to truly comprehend crystallographic defects and the chemical nature of impurities, engineers can turn to electron microscopy based methods.

One way to assess thin films is with X-rays. “High resolution X-ray measurements are useful to provide production control of the wafer crystalline quality and defects in the buffer, said Soitec’s Maleville. “Minor changes in composition of the buffer, barrier, or capping layer, as well as their layer thickness, can result in significant deviations in device performance. Thickness of the layers, in particular the top cap, barrier, and spacer layers, are typically measured by XRD. However, the throughput of XRD systems is low. Alternatively, ellipsometry offers a reasonably good throughput measurement with more data points for both development and production mode scenarios.”

Optical techniques have been the standard for thin film assessment in the semiconductor industry. Inspection equipment providers have long been on the continuation improvement always evolving journey to improve accuracy, precision and throughput. Providing better metrology tools helps device makers with process control and yield management.

“Recently, we successfully developed a non-destructive on product measurement capability for GaN epi process monitoring,” said Onto’s Hu. “It takes advantage of our advanced optical film experience and our modeling software to simultaneously measure multi-layer epi film thickness, composition, and interface roughness on product wafers.”

Fig. 4: Metrology measurements on GaN for roughness and for Al concentration. Source: Onto Innovation

Assessing the electrical characteristics — 2DEG sheet resistance, channel carrier mobility, and concentration are required for controlling the manufacturing process. A non-destructive assessment would be an improvement over currently used destructive techniques (e.g. SEM). The solutions used for other power ICs do not work for GaN HEMT. As of today, no one has come up with a commercial solution.

Inspection looks for yield impacting defects, as well as defects that affect wafer acceptance in the case of companies that provide engineered substrates.

“Defect inspection for incoming silicon wafers looks for particles, scratches, and other anomalies that might seed imperfections in the subsequent buffer and crystal growth,” said Antonio Mani, business development manager at Thermo Fisher Scientific. “After the growth of the buffer and termination layers, followed by the growth of the doped GaN layers, another set of inspections is carried out. In this case, it is more focused on the detection of cracks, other macroscopic defects (micropipes, carrots), and looking for micro-pits, which are associated to threading dislocations that have survived the buffer layer and are surfacing at the top GaN surface.”

Mani noted that follow-up inspection methods for Si and GaN devices are similar. The difference is the importance in connecting observations back to post-epi results.

More accurate defect libraries would shorten inspection time. “The lack of standardization of surface defect analysis impedes progress,” said Soitec’s Maleville. “Different tools are available on the market, while defect libraries are still being developed essentially by the different user. This lack of globally accepted method and standard defect library for surface defect analysis is slowing down the GaN surface qualification process.”

Whether it involves a manufacturing test failure or a field return, the necessary steps for determining root cause on a problematic packaged part begins with fault isolation. “Given the direct nature of the bandgap of GaN and its operating window in terms of voltage/frequency/power density, classical methods of fault isolation (e.g. optical emission spectroscopy) are forced to focus on different wavelengths and different ranges of excitation of the typical electrical defects,” said Thermo Fisher’s Mani. “Hot carrier pairs are just one example, which highlights the radical difference between GaN and silicon devices.”

In addition to fault isolation there are challenges in creating a device cross-section with focused-ion beam milling methods.

“Several challenges exist in FA for GaN power ICs,” said Zeiss’ Taraci. “In any completed device, in particular, there are numerous materials and layers present for stress mitigation/relaxation and thermal management, depending on whether we are talking enhancement- or depletion-mode devices. Length-scale can be difficult to manage as you are working with these samples, because they have structures of varying dimension present in close proximity. Many of the structures are quite unique to power GaN and can pose challenges themselves in cross-section and analyses. Beam-milling approaches have to be tailored to prevent heavy re-deposition and masking, and are dependent on material, lattice orientation, current, geometry, etc.”

Conclusion
To be successful in bringing new GaN power ICs to new application space engineers and their equipment suppliers need faster process development and a reduction in overall costs. For HEMT devices, it’s understanding the resulting layers and their material properties. This requires a host of metrology, inspection, test, and failure analysis steps to comprehend the issues, and to provide feedback data from experiments and qualifications for process and design improvements.

References

[1] M. Buffolo et al., “Review and Outlook on GaN and SiC Power Devices: Industrial State-of-the-Art, Applications, and Perspectives,” in IEEE Transactions on Electron Devices, March 2024, open access, https://ieeexplore.ieee.org/document/10388225

[2] High electron mobility transistor (HEMT) https://en.wikipedia.org/wiki/High-electron-mobility_transistor

[3] Guideline to specify a transient off-state withstand voltage robustness indicated in datasheets for lateral GaN power conversion devices, JEP186, version 1.0, December 2021. https://www.jedec.org/standards-documents/docs/jep186

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The post Controller Area Network (CAN) Overview appeared first on Semiconductor Engineering.

In the relentless pursuit of performance and miniaturization, the semiconductor industry has increasingly turned to 3D integrated circuits (3D-ICs) as a cutting-edge solution. Stacking dies in a 3D assembly offers numerous benefits, including enhanced performance, reduced power consumption, and more efficient use of space. However, this advanced technology also introduces significant thermal dissipation challenges that can impact the electrical behavior, reliability, performance, and lifespan of the chips (figure 1). For automotive applications, where safety and reliability are paramount, managing these thermal effects is of utmost importance.

Fig. 1: Illustration of a 3D-IC with heat dissipation.

3D-ICs have become particularly attractive for safety-critical devices like automotive sensors. Advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs) rely on these compact, high-performance chips to process vast amounts of sensor data in real time. Effective thermal management in these devices is a top priority to ensure that they function reliably under various operating conditions.

The thermal challenges of 3D-ICs in automotive applications

The stacked configuration of 3D-ICs inherently leads to complex thermal dynamics. In traditional 2D designs, heat dissipation occurs across a single plane, making it relatively straightforward to manage. However, in 3D-ICs, multiple active layers generate heat, creating significant thermal gradients and hotspots. These thermal issues can adversely affect device performance and reliability, which is particularly critical in automotive applications where components must operate reliably under extreme temperatures and harsh conditions.

These thermal effects in automotive 3D-ICs can impact the electrical behavior of the circuits, causing timing errors, increased leakage currents, and potential device failure. Therefore, accurate and comprehensive thermal analysis throughout the design flow is essential to ensure the reliability and performance of automotive ICs.

The importance of early and continuous thermal analysis

Traditionally, thermal analysis has been performed at the package and system levels, often as a separate process from IC design. However, with the advent of 3D-ICs, this approach is no longer sufficient.

To address the thermal challenges of 3D-ICs for automotive applications, it is crucial to incorporate die-level thermal analysis early in the design process and continue it throughout the design flow (figure 2). Early-stage thermal analysis can help identify potential hotspots and thermal bottlenecks before they become critical issues, enabling designers to make informed decisions about chiplet placement, power distribution, and cooling strategies. These early decisions reduce the risks of thermal-induced failures, improving the reliability of 3D automotive ICs.

Fig. 2: Die-level detailed thermal analysis using accurate package and boundary conditions should be fully integrated into the ASIC design flow to allow for fast thermal exploration.

Early package design, floorplanning and thermal feasibility analysis

During the initial package design and floorplanning stage, designers can use high-level power estimates and simplified models to perform thermal feasibility studies. These early analyses help identify configurations that are likely to cause thermal problems, allowing designers to rule out problematic designs before investing significant time and resources in detailed implementation.

Fig. 3: Thermal analysis as part of the package design, floorplanning and implementation flows.

For example, thermal analysis can reveal issues such as overlapping heat sources in stacked dies or insufficient cooling paths. By identifying these problems early, designers can explore alternative floorplans and adjust power distribution to mitigate thermal risks. This proactive approach reduces the likelihood of encountering critical thermal issues late in the design process, thereby shortening the overall design cycle.

Iterative thermal analysis throughout design refinement

As the design progresses and more detailed information becomes available, thermal analysis should be performed iteratively to refine the thermal model and verify that the design remains within acceptable thermal limits. At each stage of design refinement, additional details such as power maps, layout geometries and their material properties can be incorporated into the thermal model to improve accuracy.

This iterative approach lets designers continuously monitor and address thermal issues, ensuring that the design evolves in a thermally aware manner. By integrating thermal analysis with other design verification tasks, such as timing and power analysis, designers can achieve a holistic view of the design’s performance and reliability.

A robust thermal analysis tool should support various stages of the design process, providing value from initial concept to final signoff:

1. Early design planning: At the conceptual stage, designers can apply high-level power estimates to explore the thermal impact of different design options. This includes decisions related to 3D partitioning, die assembly, block and TSV floorplan, interface layer design, and package selection. By identifying potential thermal issues early, designers can make informed decisions that avoid costly redesigns later.
2. Detailed design and implementation: As designs become more detailed, thermal analysis should be used to verify that the design stays within its thermal budget. This involves analyzing the maturing package and die layout representations to account for their impact on thermally sensitive electrical circuits. Fine-grained power maps are crucial at this stage to capture hotspot effects accurately.
3. Design signoff: Before finalizing the design, it is essential to perform comprehensive thermal verification. This ensures that the design meets all thermal constraints and reliability requirements. Automated constraints checking and detailed reporting can expedite this process, providing designers with clear insights into any remaining thermal issues.
4. Connection to package-system analysis: Models from IC-level thermal analysis can be used in thermal analysis of the package and system. The integration lets designers build a streamlined flow through the entire development process of a 3D electronic product.

Tools and techniques for accurate thermal analysis

To effectively manage thermal challenges in automotive ICs, designers need advanced tools and techniques that can provide accurate and fast thermal analysis throughout the design flow. Modern thermal analysis tools are equipped with capabilities to handle the complexity of 3D-IC designs, from early feasibility studies to final signoff.

High-fidelity thermal models

Accurate thermal analysis requires high-fidelity thermal models that capture the intricate details of the 3D-IC assembly. These models should account for non-uniform material properties, fine-grained power distributions, and the thermal impact of through-silicon vias (TSVs) and other 3D features. Advanced tools can generate detailed thermal models based on the actual design geometries, providing a realistic representation of heat flow and temperature distribution.

For instance, tools like Calibre 3DThermal embeds an optimized custom 3D solver from Simcenter Flotherm to perform precise thermal analysis down to the nanometer scale. By leveraging detailed layer information and accurate boundary conditions, these tools can produce reliable thermal models that reflect the true thermal behavior of the design.

Automation and results viewing

Automation is a key feature of modern thermal analysis tools, enabling designers to perform complex analyses without requiring deep expertise in thermal engineering. An effective thermal analysis tool must offer advanced automation to facilitate use by non-experts. Key automation features include:

1. Optimized gridding: Automatically applying finer grids in critical areas of the model to ensure high resolution where needed, while using coarser grids elsewhere for efficiency.
2. Time step automation: In transient analysis, smaller time steps can be automatically generated during power transitions to capture key impacts accurately.
3. Equivalent thermal properties: Automatically reducing model complexity while maintaining accuracy by applying different bin sizes for critical (hotspot) vs non-critical regions when generating equivalent thermal properties.
4. Power map compression: Using adaptive bin sizes to compress very large power maps to improve tool performance.

5. Automated reporting: Generating summary reports that highlight key results for easy review and decision-making (figure 4).

Fig. 4: Ways to view thermal analysis results.

Automated thermal analysis tools can also integrate seamlessly with other design verification and implementation tools, providing a unified environment for managing thermal, electrical, and mechanical constraints. This integration ensures that thermal considerations are consistently addressed throughout the design flow, from initial feasibility analysis to final tape-out and even connecting with package-level analysis tools.

Real-world application

The practical benefits of integrated thermal analysis solutions are evident in real-world applications. For instance, a leading research organization, CEA, utilized an advanced thermal analysis tool from Siemens EDA to study the thermal performance of their 3DNoC demonstrator. The high-fidelity thermal model they developed showed a worst-case difference of just 3.75% and an average difference within 2% between simulation and measured data, demonstrating the accuracy and reliability of the tool (figure 5).

Fig. 5: Correlation of simulation versus measured results.

The path forward for automotive 3D-IC thermal management

As the automotive industry continues to embrace advanced technologies, the importance of accurate thermal analysis throughout the design flow of 3D-ICs cannot be overstated. By incorporating thermal analysis early in the design process and iteratively refining thermal models, designers can mitigate thermal risks, reduce design time, and enhance chip reliability.

Advanced thermal analysis tools that integrate seamlessly with the broader design environment are essential for achieving these goals. These tools enable designers to perform high-fidelity thermal analysis, automate complex tasks, and ensure that thermal considerations are addressed consistently from package design, through implementation to signoff.

By embracing these practices, designers can unlock the full potential of 3D-IC technology, delivering innovative, high-performance devices that meet the demands of today’s increasingly complex automotive applications.

For more information about die-level 3D-IC thermal analysis, read Conquer 3DIC thermal impacts with Calibre 3DThermal.

The post Ensure Reliability In Automotive ICs By Reducing Thermal Effects appeared first on Semiconductor Engineering.

In the automotive industry, AI-enabled automotive devices and systems are dramatically transforming the way SoCs are designed, making high-quality and reliable die-to-die and chip-to-chip connectivity non-negotiable. This article explains how interface IP for die-to-die connectivity, display, and storage can support new developments in automotive SoCs for the most advanced innovations such as centralized zonal architecture and integrated ADAS and IVI applications.

AI-integrated ADAS SoCs

The automotive industry is adopting a new electronic/electric (EE) architecture where a centralized compute module executes multiple applications such as ADAS and in-vehicle infotainment (IVI). With the advent of EVs and more advanced features in the car, the new centralized zonal architecture will help minimize complexity, maximize scalability, and facilitate faster decision-making time. This new architecture is demanding a new set of SoCs on advanced process technologies with very high performance. More traditional monolithic SoCs for single functions like ADAS are giving way to multi-die designs where various dies are connected in a single package and placed in a system to perform a function in the car. While such multi-die designs are gaining adoption, semiconductor companies must remain cost-conscious as these ADAS SoCs will be manufactured at high volumes for a myriad of safety levels. One example is the automated driving central compute system. The system can include modules for the sensor interface, safety management, memory control and interfaces, and dies for CPU, GPU, and AI accelerator, which are then connected via a die-to-die interface such as the Universal Chiplet Interconnect Express (UCIe). Figure 1 illustrates how semiconductor companies can develop SoCs for such systems using multi-die designs. For a base ADAS or IVI SoC, the requirement might just be the CPU die for a level 2 functional safety. A GPU die can be added to the base CPU die for a base ADAS or premium IVI function at a level 2+ driving automation. To allow more compute power for AI workloads, an NPU die can be added to the base CPU or the base CPU and GPU dies for level 3/3+ functional safety. None of these scalable scenarios are possible without a solution for die-to-die connectivity.

Fig. 1: A simplified view of automotive systems using multi-die designs.

The adoption of UCIe for automotive SoCs

The industry has come together to define, develop, and deploy the UCIe standard, a universal interconnect at the package-level. In a recent news release, the UCIe Consortium announced “updates to the standard with additional enhancements for automotive usages – such as predictive failure analysis and health monitoring – and enabling lower-cost packaging implementations.” Figure 2 shows three use cases for UCIe. The first use case is for low-latency and coherency where two Network on a Chip (NoC) are connected via UCIe. This use case is mainly for applications requiring ADAS computing power. The second automotive use case is when memory and IO are split into two separate dies and are then connected to the compute die via CXL and UCIe streaming protocols. The third automotive use case is very similar to what is seen in HPC applications where a companion AI accelerator die is connected to the main CPU die via UCIe.

Fig. 2: Examples of common and new use cases for UCIe in automotive applications.

To enable such automotive use cases, UCIe offers several advantages, all of which are supported by the Synopsys UCIe IP:

• Latency optimized architecture: Flit-Aware Die-to-Die Interface (FDI) or Raw Die-to-Die Interface (RDI) operate with local 2GHz system clock. Transmitter and receiver FIFOs accommodate phase mismatch between clock domains. There is no clock domain crossing (CDC) between the PHY and Adapter layers for minimum latency. The reference clock has the same frequency for the two dies.
• Power-optimized architecture: The transmitter provides the CMOS driver without source termination. IT offers programmable drive strength without a Feed-Forward Equalizer (FFE). The receiver provides a continuous-time linear equalizer (CTLE) without VGA and decision feedback equalizer (DFE), clock forwarding without Clock and Data Recovery (CDR), and optional receiver termination.
• Reliability and test: Signal integrity monitors track the performance of the interconnect through the chip’s lifecycle. This can monitor inaccessible paths in the multi-die package, test and repair the PHY, and execute real time reporting for preventative maintenance.

Synopsys UCIe IP is integrated with Synopsys 3DIC Compiler, a unified exploration-to-signoff platform. The combination eases package design and provides a complete set of IP deliverables, automated UCIe routing for better quality of results, and reference interposer design for faster integration.

Fig. 3: Synopsys 3DIC Compiler.

New automotive SoC design trends for IVI applications

OEMs are attracting consumers by providing the utmost in cockpit experience with high-resolution, 4K, pillar-to-pillar displays. Multi-Stream Transport (MTR) enables a daisy-chained display topology using a single port, which consists of a single GPU, one DP TX controller, and PHY, to display images on multiple screens in the car. This revision clarifies the components involved and maintains the original meaning. This daisy-chained set up simplifies the display wiring in the car. Figure 4 illustrates how connectivity in the SoC can enable multi-display environments in the car. Row 1: Multiple image sources from the application processor are fed into the daisy-chained display set up via the DisplayPort (DP) MTR interface. Row 2: Multiple image sources from the application processor are fed to the daisy-chained display set up but also to the left or right mirrors, all via the DP MTR interface. Row 3: The same set up in row 2 can be executed via the MIPI DSI or embedded DP MTR interfaces, depending on display size and power requirements.

An alternate use case is USB/DP. A single USB port can be used for silicon lifecycle management, sentry mode, test, debug, and firmware download. USB can be used to avoid the need for very large numbers of test pings, speed up test by exceeding GPIO test pin data rates, repeat manufacturing test in-system and in-field, access PVT monitors, and debug.

Fig. 4: Examples of display connectivity in software-defined vehicles.

ISO/SAE 21434 automotive cybersecurity

ISO/SAE 21434 Automotive Cybersecurity is being adopted by industry leaders as mandated by the UNECE R155 regulation. Starting in July 2024, automotive OEMs must comply with the UNECE R155 automotive cybersecurity regulation for all new vehicles in Europe, Japan, and Korea.

Automotive suppliers must develop processes that meet the automotive cybersecurity requirements of ISO/SAE 21434, addressing the cybersecurity perspective in the engineering of electrical and electronic (E/E) systems. Adopting this methodology involves embracing a cybersecurity culture which includes developing security plans, setting security goals, conducting engineering reviews and implementing mitigation strategies.

The industry is expected to move towards enabling cybersecurity risk-managed products to mitigate the risks associated with advancement in connectivity for software-defined vehicles. As a result, automotive IP needs to be ready to support these advancements.

Synopsys ARC HS4xFS Processor IP has achieved ISO/SAE 21434 cybersecurity certification by SGS-TṺV Saar, meeting stringent automotive regulatory requirements designed to protect connected vehicles from malicious cyberattacks. In addition, Synopsys has achieved certification of its IP development process to the ISO/SAE 21434 standard to help ensure its IP products are developed with a security-first mindset through every phase of the product development lifecycle.

Conclusion

The transformation to software-defined vehicles marks a significant shift in the automotive industry, bringing together highly integrated systems and AI to create safer and more efficient vehicles while addressing sophisticated user needs and vendor serviceability. New trends in the automotive industry are presenting opportunities for innovations in ADAS and IVI SoC designs. Centralized zonal architecture, multi-die design, daisy-chained displays, and integration of ADAS/IVI functions in a single SoC are among some of the key trends that the automotive industry is tracking. Synopsys is at the forefront of automotive SoC innovations with a portfolio of silicon-proven automotive IP for the highest levels of functional safety, security, quality, and reliability. The IP portfolio is developed and assessed specifically for ISO 26262 random hardware faults and ASIL D systematic. To minimize cybersecurity risks, Synopsys is developing IP products as per the ISO/SAE 21434 standard to provide automotive SoC developers a safe, reliable, and future proof solution.

The post Keeping Up With New ADAS And IVI SoC Trends appeared first on Semiconductor Engineering.

By Reetika and Sulabh Kumar Khare, Siemens EDA DI SW

To meet low-power and high-performance requirements, system on chip (SoC) designs are equipped with several asynchronous and soft reset signals. These reset signals help to safeguard software and hardware functional safety as they can be asserted to speedily recover the system onboard to an initial state and clear any pending errors or events.

By definition, a reset domain crossing (RDC) occurs when a path’s transmitting flop has an asynchronous reset, and the receiving flop either has a different asynchronous reset than the transmitting flop or has no reset. The multitude of asynchronous reset sources found in today’s complex automotive designs means there are a large number of RDC paths, which can lead to systematic faults and hence cause data-corruption, glitches, metastability, or functional failures — along with other issues.

This issue is not covered by standard, static verification methods, such as clock domain crossing (CDC) analysis. Therefore, a proper reset domain crossing verification methodology is required to prevent errors in the reset design during the RTL verification stage.

A soft reset is an internally generated reset (register/latch/black-box output is used as a reset) that allows the design engineer to reset a specific portion of the design (specific module/subsystem) without affecting the entire system. Design engineers frequently use a soft reset mechanism to reset/restart the device without fully powering it off, as this helps to conserve power by selectively resetting specific electronic components while keeping others in an operational state. A soft reset typically involves manipulating specific registers or signals to trigger the reset process. Applying soft resets is a common technique used to quickly recover from a problem or test a specific area of the design. This can save time during simulation and verification by allowing the designer to isolate and debug specific issues without having to restart the entire simulation. Figure 1 shows a simple soft reset and its RTL to demonstrate that SoftReg is a soft reset for flop Reg.

Fig. 1: SoftReg is a soft reset for register Reg.

This article presents a systematic methodology to identify RDCs, with different soft resets, that are unsafe, even though the asynchronous reset domain is the same on the transmitter and receiver ends. Also, with enough debug aids, we will identify the safe RDCs (safe from metastability only if it meets the static timing analysis), with different asynchronous reset domains, that help to avoid silicon failures and minimize false crossing results. As a part of static analysis, this systematic methodology enables designers to intelligently identify critical reset domain bugs associated with soft resets.

A methodology to identify critical reset domain bugs

With highly complex reset architectures in automotive designs, there arises the need for a proper verification method to detect RDC issues. It is essential to detect unsafe RDCs systematically and apply appropriate synchronization techniques to tackle the issues that may arise due to delays in reset paths caused by soft resets. Thus designers can ensure proper operation of their designs and avoid the associated risks. By handling RDCs effectively, designers can mitigate potential issues and enhance the overall robustness and performance of a design. This systematic flow involves several steps to assist in RDC verification closure using standard RDC verification tools (see figure 2).

Fig. 2: Flowchart of methodology for RDC verification.

Specification of clock and reset signals

Signals that are intended to generate a clock and reset pulse should be specified by the user as clock or reset signals, respectively, during the set-up step in RDC verification. By specifying signals as clocks or resets (according to their expected behavior), designers can perform design rule checking and other verification checks to ensure compliance with clock and reset related guidelines and standards as well as best practices. This helps identify potential design issues and improve the overall quality of the design by reducing noise in the results.

Clock detection

Ideally, design engineers should define the clock signals and then the verification tool should trace these clocks down to the leaf clocks. Unfortunately, with complex designs, this is not possible as the design might have black boxes that originate clocks, or it may have some combinational logic in the clock signals that do not cover all the clocks specified by the user. All the un-specified clocks need to be identified and mapped to the user-specified primary clocks. An exhaustive detection of clocks is required in RDC verification, as potential metastability may occur if resets are used in different clock domains than the sequential element itself, leading to critical bugs.

Reset detection

Ideally, design engineers should define the reset signals, but again, due to the complexity of automotive and other modern designs, it is not possible to specify all the reset signals. Therefore a specialized verification tool is required for detection of resets. All the localized, black-box, gated, and primary resets need to be identified, and based on their usage in the RTL, they should be classified as synchronous, asynchronous, or dual type and then mapped to the user-specified primary resets.

Soft reset detection

The soft resets — i.e., the internally generated resets by flops and latches — need to be systematically detected as they can cause critical metastability issues when used in different clock domains, and they require static timing analysis when used in the same clock domain. Detecting soft resets helps identify potential metastability problems and allows designers to apply proper techniques for resolving these issues.

Reset tree analysis

Analysis of reset trees helps designers identify issues early in the design process, before RDC analysis. It helps to highlight some important errors in the reset design that are not commonly caught by lint tools. These include:

• Dual synchronicity reset signals, i.e., the reset signal with a sample synchronous reset flop and a sample asynchronous reset flop
• An asynchronous set/reset signal used as a data signal can result in incorrect data sampling because the reset state cannot be controlled

Reset domain crossing analysis

This step involves analyzing a design to determine the logic across various reset domains and identify potential RDCs. The analysis should also identify common reset sequences of asynchronous and soft reset sources at the transmitter and receiver registers of the crossings to avoid detection of false crossings that might appear as potential issues due to complex combinations of reset sources. False crossings are where a transmitter register and receiver register are asserted simultaneously due to dependencies among the reset assertion sequences, and as a result, any metastability that might occur on the receiver end is mitigated.

Analyze and fix RDC issues

The concluding step is to analyze the results of the verification steps to verify if data paths crossing reset domains are safe from metastability. For the RDCs identified as unsafe — which may occur either due to different asynchronous reset domains at the transmitter and receiver ends or due to the soft reset being used in a different clock domain than the sequential element itself — design engineers can develop solutions to eliminate or mitigate metastability by restructuring the design, modifying reset synchronization logic, or adjusting the reset ordering. Traditionally safe RDCs — i.e., crossings where a soft reset is used in the same clock domain as the sequential element itself — need to be verified using static timing analysis.

Figure 3 presents our proposed flow for identifying and eliminating metastability issues due to soft resets. After implementing the RDC solutions, re-verify the design to ensure that the reset domain crossing issues have been effectively addressed.

Fig. 3: Flowchart for proposed methodology to tackle metastability issues due to soft resets.

This methodology was used on a design with 374,546 register bits, 8 latch bits, and 45 RAMs. The Questa RDC verification tool using this new methodology identified around 131 reset domains, which consisted of 19 asynchronous domains defined by the user, as well as 81 asynchronous reset domains inferred by the tool.

The first run analyzed data paths crossing asynchronous reset domains without any soft reset analysis. It reported nearly 40,000 RDC crossings (as shown in table 1).

Reset domain crossings without soft reset analysis	Severity	Number of crossings
Reset domain crossing from a reset to a reset	Violation	28408
Reset domain crossing from a reset to non-reset	Violation	11235

Table 1: RDC analysis without soft resets.

In the second run, we did soft reset analysis and detected 34 soft resets, which resulted in additional violations for RDC paths with transmitter soft reset sources in different clock domains. These were critical violations that were missed in the initial run. Also, some RDC violations were converted to cautions (RDC paths with a transmitter soft reset in the same clock domain) as these paths would be safe from metastability as long as they meet the setup time window (as shown in table 2).

Reset domain crossings with soft reset analysis	Severity	Number of crossings
Reset domain crossing from a reset to a reset	Violation	26957
Reset domain crossing from a reset to non-reset	Violation	10523
Reset domain crossing with tx reset source in different clock	Violation	880
Reset domain crossing from a reset to Rx with same clock	Caution	2412

Table 2: RDC analysis with soft resets.

To gain a deeper understanding of RDC, metastability, and soft reset analysis in the context of this new methodology, please download the full paper Techniques to identify reset metastability issues due to soft resets.

The post Reset Domain Crossing Verification appeared first on Semiconductor Engineering.

The automotive industry stands at the brink of a profound transformation fueled by the relentless march of technological innovation. Gone are the days of the traditional, one-size-fits-all system-on-chip (SoC) design framework. Today, we are witnessing a paradigm shift towards a more modular approach that utilizes diverse chiplets, each optimized for specific functionalities. This evolution promises to enhance the automotive system’s flexibility and efficiency and revolutionize how vehicles are designed, built, and operated.

At the heart of this transformation lies the Universal Chiplet Interconnect Express (UCIe), a groundbreaking standard introduced in March 2022. UCIe is designed to drastically simplify the integration process across different chiplets from various manufacturers by standardizing die-to-die connections. This initiative caters to a critical need within the industry for a modular and scalable semiconductor architecture, thus setting the stage for unparalleled innovation in automotive electronics.

Understanding the core benefit of UCIe

UCIe isn’t merely about facilitating smoother communication between chiplets; it’s a visionary standard that ensures interoperability, reduces design complexity and costs, and, crucially, supports the seamless incorporation of chiplets into cohesive packages. Developed through the collaborative efforts of the UCIe consortium, this standard enables the use of chiplets from different vendors, thereby fostering a highly customizable and scalable solution. This mainly benefits the automotive sector, where high performance, reliability, and efficiency demand is paramount.

The pivotal role of UCIe in automotive electronics

Implementing UCIe within automotive electronics unlocks a plethora of advantages. Foremost among these is the ability to design more compact, powerful, and energy-efficient electronic systems. UCIe offers savings in energy per bit compared to other serial or parallel interfaces.

Given the increasing complexity and functionality of modern vehicles — which can be likened to data centers on wheels — the modular design principle of UCIe is invaluable. It facilitates the addition of new functionalities and ensures that automotive electronics can seamlessly adapt to and incorporate emerging technologies and standards.

Secondly, the adoption of UCIe marks a significant stride toward sustainable electronic design within the automotive industry. By promoting the reuse and integration of chiplets across different platforms and vehicle models, UCIe significantly mitigates electronic waste and streamlines the lifecycle management of electronic components. This benefits manufacturers regarding cost and efficiency and aligns with broader industry trends focused on sustainability and environmental stewardship.

Offerings and solutions

Cadence offers a range of Intellectual Properties (IPs), Electronic Design Automation (EDA) tools, and 3D Integrated Circuits (ICs) tailored for chiplets.

Since the beginning of UCIe, Cadence has engaged with over 100 customers and enables automotive solutions with UCIe and its working groups. The implementation features for automotive protect the mainband, and Cadence also adds protection for the sideband, particularly for the automotive industry. UCIe is being developed across multiple foundries, process nodes, and standard and advanced packaging enablement types.

Cadence has a history of chiplet experience with an interface IP portfolio, including our chiplet die-to-die interconnects, including the UCIe IP, and proprietary 40G UltraLink D2D PHY that enables SoC providers to deliver more customized solutions that offer higher performance and yields while also shortening development cycles and reducing costs through greater IP reuse. Cadence’s UCIe PHY solutions facilitate high-speed communication and interoperability among chiplets. Additionally, the Cadence Simulation VIP for UCIe ensures that systems using UCIe are reliable and performant, addressing one of the industry’s key concerns.

The Cadence UCIe PHY is a high-bandwidth, low-power, and low-latency die-to-die solution that enables multi-die system in package integration for high-performance compute, AI/ML, 5G, automotive and networking applications. The UCIe physical layer includes link initialization, training, power management states, lane mapping, lane reversal, and scrambling. The UCIe controller includes the die-to-die adapter layer and the protocol layer. The adapter layer ensures reliable transfer through link state management, protocol, and flit formats parameter negotiation. The UCIe architecture supports multiple standard protocols such as PCIe, CXL and streaming raw mode.

For interface IP, we usually create various test chips using different process technologies and measure the silicon in the lab to prove that our controller and PHY IPs fully comply with the standard. Hence, we designed a test chip with seven chiplets connected via UCIe over different interconnect distances. To learn more, click here.

The Cadence Verification IP (VIP) for Universal Chiplet Interconnect Express (UCIe) is designed for easy integration in test benches at the IP, system-on-chip (SoC), and system level. The VIP for UCIe runs on all simulators and supports SystemVerilog and the widely adopted Universal Verification Methodology (UVM). This enables verification teams to reduce the time spent on environment development and redirect it to cover a larger verification space, accelerate verification closure, and ensure end-product quality. With a layered architecture and powerful callback mechanism, verification engineers can verify UCIe features at each functional layer (PHY, D2D, Protocol) and create highly targeted designs while using the latest design methodologies for random testing to cover a larger verification space. The VIP for UCIe can be used as a standalone stack or layered with PCIe VIP.

Challenges and future prospects

The transition to chiplet-based designs and the widespread adoption of the UCIe standard are not without their challenges. Critical concerns include functional safety, reliability, quality, security, thermal management, and mechanical stress. Hence, ensuring dependable high-speed interconnects between chiplets necessitates ongoing research and development. Successful implementation also hinges on industry-wide collaboration and establishing a robust chiplet ecosystem that supports the UCIe standard.

UCIe stands as a pioneering innovation poised to redefine automotive electronics. By offering a scalable and flexible framework, UCIe promises to enhance in-vehicle electronic systems’ performance, efficiency, and versatility and marks a significant milestone in the automotive industry’s evolution. As we look to the future, the impact of UCIe in the automotive sector is poised to be profound, turning vehicles into dynamic platforms that continuously adapt to technological advancements and user needs. The realization of modular, customizable designs underscored by efficiency, performance, and innovation heralds the dawn of a new era in semiconductor development, one where the possibilities are as boundless as the technological horizon.

The post UCIe And Automotive Electronics: Pioneering The Chiplet Revolution appeared first on Semiconductor Engineering.

The automotive industry stands at the brink of a profound transformation fueled by the relentless march of technological innovation. Gone are the days of the traditional, one-size-fits-all system-on-chip (SoC) design framework. Today, we are witnessing a paradigm shift towards a more modular approach that utilizes diverse chiplets, each optimized for specific functionalities. This evolution promises to enhance the automotive system’s flexibility and efficiency and revolutionize how vehicles are designed, built, and operated.

At the heart of this transformation lies the Universal Chiplet Interconnect Express (UCIe), a groundbreaking standard introduced in March 2022. UCIe is designed to drastically simplify the integration process across different chiplets from various manufacturers by standardizing die-to-die connections. This initiative caters to a critical need within the industry for a modular and scalable semiconductor architecture, thus setting the stage for unparalleled innovation in automotive electronics.

Understanding the core benefit of UCIe

UCIe isn’t merely about facilitating smoother communication between chiplets; it’s a visionary standard that ensures interoperability, reduces design complexity and costs, and, crucially, supports the seamless incorporation of chiplets into cohesive packages. Developed through the collaborative efforts of the UCIe consortium, this standard enables the use of chiplets from different vendors, thereby fostering a highly customizable and scalable solution. This mainly benefits the automotive sector, where high performance, reliability, and efficiency demand is paramount.

The pivotal role of UCIe in automotive electronics

Implementing UCIe within automotive electronics unlocks a plethora of advantages. Foremost among these is the ability to design more compact, powerful, and energy-efficient electronic systems. UCIe offers savings in energy per bit compared to other serial or parallel interfaces.

Given the increasing complexity and functionality of modern vehicles — which can be likened to data centers on wheels — the modular design principle of UCIe is invaluable. It facilitates the addition of new functionalities and ensures that automotive electronics can seamlessly adapt to and incorporate emerging technologies and standards.

Secondly, the adoption of UCIe marks a significant stride toward sustainable electronic design within the automotive industry. By promoting the reuse and integration of chiplets across different platforms and vehicle models, UCIe significantly mitigates electronic waste and streamlines the lifecycle management of electronic components. This benefits manufacturers regarding cost and efficiency and aligns with broader industry trends focused on sustainability and environmental stewardship.

Offerings and solutions

Cadence offers a range of Intellectual Properties (IPs), Electronic Design Automation (EDA) tools, and 3D Integrated Circuits (ICs) tailored for chiplets.

Since the beginning of UCIe, Cadence has engaged with over 100 customers and enables automotive solutions with UCIe and its working groups. The implementation features for automotive protect the mainband, and Cadence also adds protection for the sideband, particularly for the automotive industry. UCIe is being developed across multiple foundries, process nodes, and standard and advanced packaging enablement types.

Cadence has a history of chiplet experience with an interface IP portfolio, including our chiplet die-to-die interconnects, including the UCIe IP, and proprietary 40G UltraLink D2D PHY that enables SoC providers to deliver more customized solutions that offer higher performance and yields while also shortening development cycles and reducing costs through greater IP reuse. Cadence’s UCIe PHY solutions facilitate high-speed communication and interoperability among chiplets. Additionally, the Cadence Simulation VIP for UCIe ensures that systems using UCIe are reliable and performant, addressing one of the industry’s key concerns.

The Cadence UCIe PHY is a high-bandwidth, low-power, and low-latency die-to-die solution that enables multi-die system in package integration for high-performance compute, AI/ML, 5G, automotive and networking applications. The UCIe physical layer includes link initialization, training, power management states, lane mapping, lane reversal, and scrambling. The UCIe controller includes the die-to-die adapter layer and the protocol layer. The adapter layer ensures reliable transfer through link state management, protocol, and flit formats parameter negotiation. The UCIe architecture supports multiple standard protocols such as PCIe, CXL and streaming raw mode.

For interface IP, we usually create various test chips using different process technologies and measure the silicon in the lab to prove that our controller and PHY IPs fully comply with the standard. Hence, we designed a test chip with seven chiplets connected via UCIe over different interconnect distances. To learn more, click here.

The Cadence Verification IP (VIP) for Universal Chiplet Interconnect Express (UCIe) is designed for easy integration in test benches at the IP, system-on-chip (SoC), and system level. The VIP for UCIe runs on all simulators and supports SystemVerilog and the widely adopted Universal Verification Methodology (UVM). This enables verification teams to reduce the time spent on environment development and redirect it to cover a larger verification space, accelerate verification closure, and ensure end-product quality. With a layered architecture and powerful callback mechanism, verification engineers can verify UCIe features at each functional layer (PHY, D2D, Protocol) and create highly targeted designs while using the latest design methodologies for random testing to cover a larger verification space. The VIP for UCIe can be used as a standalone stack or layered with PCIe VIP.

Challenges and future prospects

The transition to chiplet-based designs and the widespread adoption of the UCIe standard are not without their challenges. Critical concerns include functional safety, reliability, quality, security, thermal management, and mechanical stress. Hence, ensuring dependable high-speed interconnects between chiplets necessitates ongoing research and development. Successful implementation also hinges on industry-wide collaboration and establishing a robust chiplet ecosystem that supports the UCIe standard.

UCIe stands as a pioneering innovation poised to redefine automotive electronics. By offering a scalable and flexible framework, UCIe promises to enhance in-vehicle electronic systems’ performance, efficiency, and versatility and marks a significant milestone in the automotive industry’s evolution. As we look to the future, the impact of UCIe in the automotive sector is poised to be profound, turning vehicles into dynamic platforms that continuously adapt to technological advancements and user needs. The realization of modular, customizable designs underscored by efficiency, performance, and innovation heralds the dawn of a new era in semiconductor development, one where the possibilities are as boundless as the technological horizon.

The post UCIe And Automotive Electronics: Pioneering The Chiplet Revolution appeared first on Semiconductor Engineering.

The number of sensors in automobiles is growing rapidly alongside new safety features and increasing levels of autonomy. The challenge is integrating them in a way that makes sense, because these sensors are optimized for different types of data, sometimes with different resolution requirements even for the same type of data, and frequently with very different latency, power consumption, and reliability requirements. Pulin Desai, group director for product marketing, management and business development at Cadence, talks about challenges with sensor fusion, the growing importance of four-dimensional sensing, what’s needed to future-proof sensor designs, and the difficulty of integrating one or more software stacks with conflicting requirements.

The post Sensor Fusion Challenges In Automotive appeared first on Semiconductor Engineering.

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Automotive processors are rapidly adopting advanced process nodes. NXP announced the development of 5 nm automotive processors in 2020 [1], Mobileye announced EyeQ Ultra using 5 nm technology during CES 2022 [2], and TSMC announced its “Auto Early” 3 nm processes in 2023 [3]. In the past, the automotive industry was slow to adopt the latest semiconductor technologies due to reliability concerns and lack of a compelling need. Not anymore.

The use of advanced processes necessitates the use of advanced packaging as seen in high performance computing (HPC) and mobile applications because [4][5]:

1. While transistor density has skyrocketed, I/O density has not increased proportionally and is holding back chip size reductions.
2. Processors have heterogeneous, specialized blocks to support today’s workloads.
3. Maximum chip sizes are limited by the slowdown of transistor scaling, photo reticle limits and lower yields.
4. Cost per transistor improvements have slowed down with advanced nodes.
5. Off-package dynamic random-access memory (DRAM) throttles memory bandwidth.

These have been drivers for the use of advanced packages like fan-out in mobile and 2.5D/3D in HPC. In addition, these drivers are slowly but surely showing up in automotive compute units in a variety of automotive architectures as well (see figure 1).

Fig. 1: Vehicle E/E architectures. (Image courtesy of Amkor Technology)

Vehicle electrical/electronic (E/E) architectures have evolved from 100+ distributed electronic control units (ECUs) to 10+ domain control units (DCUs) [6]. The most recent architecture introduces zonal or zone ECUs that are clustered in physical locations in cars and connect to powerful central computing units for processing. These newer architectures improve scalability, cost, and reliability of software-defined vehicles (SDVs) [7]. The processors in each of these architectures are more complex than those in the previous generation.

Multiple cameras, radar, lidar and ultrasonic sensors and more feed data into the compute units. Processing and inferencing this data require specialized functional blocks on the processor. For example, the Tesla Full Self-Driving (FSD) HW 3.0 system on chip (SoC) has central processing units (CPUs), graphic processing units (GPUs), neural network processing units, Low-Power Double Data Rate 4 (LPDDR4) controllers and other functional blocks – all integrated on a single piece of silicon [8]. Similarly, Mobileye EyeQ6 has functional blocks of CPU clusters, accelerator clusters, GPUs and an LPDDR5 interface [9]. As more functional blocks are introduced, the chip size and complexity will continue to increase. Instead of a single, monolithic silicon chip, a chiplet approach with separate functional blocks allows intellectual property (IP) reuse along with optimal process nodes for each functional block [10]. Additionally, large, monolithic pieces of silicon built on advanced processes tend to have yield challenges, which can also be overcome using chiplets.

Current advanced driver-assistance systems (ADAS) applications require a DRAM bandwidth of less than 60GB/s, which can be supported with standard double data rate (DDR) and LPDDR solutions. However, ADAS Level 4 and Level 5 will need up to 1024 GB/s memory bandwidth, which will require the use of solutions such as Graphic DDR (GDDR) or High Bandwidth Memory (HBM) [11][12].

Fig. 2: Automotive compute package roadmap. (Image courtesy of Amkor Technology)

Automotive processors have been using Flip Chip BGA (FCBGA) packages since 2010. FCBGA has become the mainstay of several automotive SoCs, such as EyeQ from Mobileye, Tesla FSD and NVIDIA Drive. Consumer applications of FCBGA packaging started around 1995 [13], so it took more than 15 years for this package to be adopted by the automotive industry. Computing units in the form of multichip modules (MCMs) or System-in-Package (SiP) have also been in automotive use since the early 2010s for infotainment processors. The use of MCMs is likely to increase in automotive compute to enable components like the SoC, DRAM and power management integrated circuit (PMIC) to communicate with each other without sending signals off-package.

As cars move to a central computing architecture, the SoCs will become more complex and run into size and cost challenges. Splitting these SoCs into chiplets becomes a logical solution and packaging these chiplets using fan-out or 2.5D packages becomes necessary. Just as FCBGA and MCMs transitioned into automotive from non-automotive applications, so will fan-out and 2.5D packaging for automotive compute processors (see figure 2). The automotive industry is cautious but the abovementioned architecture changes are pushing faster adoption of advanced packages. Materials, processes, and factory controls are key considerations for successful qualification of these packages in automotive compute applications.

In summary, the automotive industry is adopting advanced semiconductor technologies, such as 5 nm and 3 nm processes, which require the use of advanced packaging due to limitations in I/O density, chip size reductions, and memory bandwidth. Processors in the latest vehicle E/E architectures are more complex and require specialized functional blocks to process data from multiple sensors. As cars move to the central computing architecture, the SoCs will become more complex and run into size and cost challenges. Splitting these SoCs into chiplets becomes a logical solution and packaging these chiplets using fan-out or 2.5D technology becomes necessary.

Sources

1. NXP. “NXP Selects TSMC 5nm Process for Next-Generation High-Performance Automotive Platform.” NXP, https://www.nxp.com/company/about-nxp/nxp-selects-tsmc-5nm-process-for-next-generation-high-performance-automotive-platform:NW-TSMC-5NM-HIGH-PERFORMANCE.
2. Mobileye. “Mobileye at CES 2022.” Mobileye, https://www.mobileye.com/news/mobileye-ces-2022-tech-news/.
3. Business Wire. “TSMC Showcases New Technology Developments at 2023 Technology Symposium.” Business Wire, https://www.businesswire.com/news/home/20230426005359/en/TSMC-Showcases-New-Technology-Developments-at-2023-Technology-Symposium.
4. Swaminathan, Raja. “Advanced Packaging: Enabling Moore’s Law’s Next Frontier Through Heterogeneous Integration.” HotChips33, https://hc33.hotchips.org/assets/program/tutorials/2021%20Hot%20Chips%20AMD%20Advanced%20Packaging%20Swaminathan%20Final%20%2020210820.pdf
5. SemiAnalysis. “Advanced Packaging Part 1” SemiAnalysis, https://www.semianalysis.com/p/advanced-packaging-part-1-pad-limited?utm_source=%2Fsearch%2Fadvanced%2520packaging&utm_medium=reader2.
6. McKinsey & Company. “Getting Ready for Next-Generation EE Architecture with Zonal Compute.” McKinsey & Company, https://www.mckinsey.com/industries/semiconductors/our-insights/getting-ready-for-next-generation-ee-architecture-with-zonal-compute.
7. NXP. “How Zonal E/E Architectures with Ethernet are Enabling Software-Defined Vehicles.” NXP, https://www.nxp.com/company/blog/how-zonal-e-e-architectures-with-ethernet-are-enabling-software-defined-vehicles:BL-HOW-ZONAL-EE-ARCHITECTURES.
8. WikiChip. “Tesla (Car Company)/FSD Chip.” WikiChip, https://en.wikichip.org/wiki/tesla_(car_company)/fsd_chip.
9. Mobileye. “EyeQ Chip.” Mobileye, https://www.mobileye.com/technology/eyeq-chip/.
10. Ziadeh, Bassam. “Driving Adoption of Advanced IC Packaging in Automotive Applications.” Presentation at IMAPS DPC, March 2023. General Motors, Fountain Hills AZ, March 16, 2023.
11. K Matthias Jung and Norbert Wehn. “Driving Against the Memory Wall: The Role of Memory for Autonomous Driving.” Fraunhofer IESE, Kaiserslautern, Germany, and Microelectronic Systems Design Research Group, University of Kaiserslautern, Kaiserslautern, Germany. Kluedo, https://kluedo.ub.rptu.de/frontdoor/deliver/index/docId/5286/file/_memory.pdf.
12. Micron. “Cinco de Play: Memory – Is That Critical to Autonomous Driving?” Micron, https://www.micron.com/about/blog/2017/october/cinco-play-memory-is-that-critical-to-autonomous-driving.
13. McKinsey & Company. “Advanced Chip Packaging: How Manufacturers Can Play to Win.” McKinsey & Company, https://www.mckinsey.com/industries/semiconductors/our-insights/advanced-chip-packaging-how-manufacturers-can-play-to-win.

The post Powering The Automotive Revolution: Advanced Packaging For Next-Generation Vehicle Computing appeared first on Semiconductor Engineering.

The need to mitigate climate change is driving a need to electrify our infrastructure, vehicles, and appliances, which can then be charged and powered by renewable energy sources. The most visible and impactful electrification is now under way for electric vehicles (EVs). Beyond the transition to electric engines, several new features and technologies are driving the electrification of vehicles. The number of sensors in a vehicle is skyrocketing, driven by autonomous driving and other safety features, while a modern software-defined vehicle (SDV) is electrifying everything from air-conditioned seats to self-parking technology.

An important technology for EVs and SDVs is power modules. These are super high-voltage devices that convert one form of electricity to another (e.g., AC to DC), which is necessary to convert the vehicle battery energy to a current that can run the vehicles electrical system, including the drive train. These modules demand the highest power loads and are rated at 1000s of voltages – and the design of power devices, which are the fundamental electronic component of the power modules, is crucial, as a bad design can lead to catastrophe events.

Power devices, much more than other types of electrical devices, are designed for specific applications. In comparison, logic transistors can be used in everything from toasters to smartphones. Not only does the architecture of power devices change at higher voltages, different power ratings, or higher switching frequencies as needed, but the material can change as well.

New power requirements need wide-band gap materials

To meet new and future power demands for EVs, electric infrastructure, and other novel electrical systems, wide-band gap (WBG) materials are being developed and introduced. Silicon carbide (SiC) IGBTs are now available and being deployed, while gallium arsenide (GaN) HEMTs are a promising technology that is in the development stage.

Power density vs. switching frequency of power devices based on different materials.

Continuing with our EV example, SiC inverters can generally increase the potential range by approximately 10%, even after accounting for other design considerations. In addition, increasing the drive train voltage from 400V range to 800V can reduce the charging speeds by half. These voltages are only possible to realize with wide-band gap materials like SiC-based power devices. Tesla introduced SiC MOSFETs into its Model S back in 2018. Since then, numerous automotive manufacturers have also adopted SiC in their EVs, including Hyundai and BMW, for example.

GaN still has many design hurdles to overcame to improve reliability and decrease cost – but if it can be made affordable, perhaps the next realization of EVs will allow for charging in seconds with ranges of thousands of miles.

Simulating power devices

Because of the huge number of design parameters, simulation is important in the design of power devices. One crucial part for device design is the calculation of the breakdown voltage – the voltage at which the device can essentially melt, or catch fire, but will never operate again. These simulations need to be highly physics-based and capture the mechanisms by which electrons can be released or absorbed by the crystal lattice of these materials. The increasing band gaps in WBG materials like SiC and GaN increase the breakdown voltage. In addition, these materials have a smaller effective electron mass (i.e., the mass of an electron in a material dictates how fast it will move in an electric field) – which makes the switching frequency in devices based on these WBG materials faster.

A critical area of all electronics design is variability and reliability. Device performance needs to be stable and last a long time. A key factor for variability and reliability is defects in the crystal lattice. These defects, or traps, act as charge centers that can drastically impact how well a device works. Simulation can also help to identify the types of traps, providing a mechanistic understanding of how the traps will impact the device physics. Recently, Synopsys issued a paper using first-principles quantum solutions to characterize specific traps in SiC with QuantumATK.

Going forward, wind energy, solar, home appliances, and even the electric grid itself are going to need new devices with different structures and materials. The future is extremely exciting for power devices, which can be found in our EVs and will soon power a huge range of applications across our society.

The post Enabling New Applications With SiC IGBT And GaN HEMT For Power Module Design appeared first on Semiconductor Engineering.

Automotive processors are rapidly adopting advanced process nodes. NXP announced the development of 5 nm automotive processors in 2020 [1], Mobileye announced EyeQ Ultra using 5 nm technology during CES 2022 [2], and TSMC announced its “Auto Early” 3 nm processes in 2023 [3]. In the past, the automotive industry was slow to adopt the latest semiconductor technologies due to reliability concerns and lack of a compelling need. Not anymore.

The use of advanced processes necessitates the use of advanced packaging as seen in high performance computing (HPC) and mobile applications because [4][5]:

1. While transistor density has skyrocketed, I/O density has not increased proportionally and is holding back chip size reductions.
2. Processors have heterogeneous, specialized blocks to support today’s workloads.
3. Maximum chip sizes are limited by the slowdown of transistor scaling, photo reticle limits and lower yields.
4. Cost per transistor improvements have slowed down with advanced nodes.
5. Off-package dynamic random-access memory (DRAM) throttles memory bandwidth.

These have been drivers for the use of advanced packages like fan-out in mobile and 2.5D/3D in HPC. In addition, these drivers are slowly but surely showing up in automotive compute units in a variety of automotive architectures as well (see figure 1).

Fig. 1: Vehicle E/E architectures. (Image courtesy of Amkor Technology)

Vehicle electrical/electronic (E/E) architectures have evolved from 100+ distributed electronic control units (ECUs) to 10+ domain control units (DCUs) [6]. The most recent architecture introduces zonal or zone ECUs that are clustered in physical locations in cars and connect to powerful central computing units for processing. These newer architectures improve scalability, cost, and reliability of software-defined vehicles (SDVs) [7]. The processors in each of these architectures are more complex than those in the previous generation.

Multiple cameras, radar, lidar and ultrasonic sensors and more feed data into the compute units. Processing and inferencing this data require specialized functional blocks on the processor. For example, the Tesla Full Self-Driving (FSD) HW 3.0 system on chip (SoC) has central processing units (CPUs), graphic processing units (GPUs), neural network processing units, Low-Power Double Data Rate 4 (LPDDR4) controllers and other functional blocks – all integrated on a single piece of silicon [8]. Similarly, Mobileye EyeQ6 has functional blocks of CPU clusters, accelerator clusters, GPUs and an LPDDR5 interface [9]. As more functional blocks are introduced, the chip size and complexity will continue to increase. Instead of a single, monolithic silicon chip, a chiplet approach with separate functional blocks allows intellectual property (IP) reuse along with optimal process nodes for each functional block [10]. Additionally, large, monolithic pieces of silicon built on advanced processes tend to have yield challenges, which can also be overcome using chiplets.

Current advanced driver-assistance systems (ADAS) applications require a DRAM bandwidth of less than 60GB/s, which can be supported with standard double data rate (DDR) and LPDDR solutions. However, ADAS Level 4 and Level 5 will need up to 1024 GB/s memory bandwidth, which will require the use of solutions such as Graphic DDR (GDDR) or High Bandwidth Memory (HBM) [11][12].

Fig. 2: Automotive compute package roadmap. (Image courtesy of Amkor Technology)

Automotive processors have been using Flip Chip BGA (FCBGA) packages since 2010. FCBGA has become the mainstay of several automotive SoCs, such as EyeQ from Mobileye, Tesla FSD and NVIDIA Drive. Consumer applications of FCBGA packaging started around 1995 [13], so it took more than 15 years for this package to be adopted by the automotive industry. Computing units in the form of multichip modules (MCMs) or System-in-Package (SiP) have also been in automotive use since the early 2010s for infotainment processors. The use of MCMs is likely to increase in automotive compute to enable components like the SoC, DRAM and power management integrated circuit (PMIC) to communicate with each other without sending signals off-package.

As cars move to a central computing architecture, the SoCs will become more complex and run into size and cost challenges. Splitting these SoCs into chiplets becomes a logical solution and packaging these chiplets using fan-out or 2.5D packages becomes necessary. Just as FCBGA and MCMs transitioned into automotive from non-automotive applications, so will fan-out and 2.5D packaging for automotive compute processors (see figure 2). The automotive industry is cautious but the abovementioned architecture changes are pushing faster adoption of advanced packages. Materials, processes, and factory controls are key considerations for successful qualification of these packages in automotive compute applications.

In summary, the automotive industry is adopting advanced semiconductor technologies, such as 5 nm and 3 nm processes, which require the use of advanced packaging due to limitations in I/O density, chip size reductions, and memory bandwidth. Processors in the latest vehicle E/E architectures are more complex and require specialized functional blocks to process data from multiple sensors. As cars move to the central computing architecture, the SoCs will become more complex and run into size and cost challenges. Splitting these SoCs into chiplets becomes a logical solution and packaging these chiplets using fan-out or 2.5D technology becomes necessary.

Sources

1. NXP. “NXP Selects TSMC 5nm Process for Next-Generation High-Performance Automotive Platform.” NXP, https://www.nxp.com/company/about-nxp/nxp-selects-tsmc-5nm-process-for-next-generation-high-performance-automotive-platform:NW-TSMC-5NM-HIGH-PERFORMANCE.
2. Mobileye. “Mobileye at CES 2022.” Mobileye, https://www.mobileye.com/news/mobileye-ces-2022-tech-news/.
3. Business Wire. “TSMC Showcases New Technology Developments at 2023 Technology Symposium.” Business Wire, https://www.businesswire.com/news/home/20230426005359/en/TSMC-Showcases-New-Technology-Developments-at-2023-Technology-Symposium.
4. Swaminathan, Raja. “Advanced Packaging: Enabling Moore’s Law’s Next Frontier Through Heterogeneous Integration.” HotChips33, https://hc33.hotchips.org/assets/program/tutorials/2021%20Hot%20Chips%20AMD%20Advanced%20Packaging%20Swaminathan%20Final%20%2020210820.pdf
5. SemiAnalysis. “Advanced Packaging Part 1” SemiAnalysis, https://www.semianalysis.com/p/advanced-packaging-part-1-pad-limited?utm_source=%2Fsearch%2Fadvanced%2520packaging&utm_medium=reader2.
6. McKinsey & Company. “Getting Ready for Next-Generation EE Architecture with Zonal Compute.” McKinsey & Company, https://www.mckinsey.com/industries/semiconductors/our-insights/getting-ready-for-next-generation-ee-architecture-with-zonal-compute.
7. NXP. “How Zonal E/E Architectures with Ethernet are Enabling Software-Defined Vehicles.” NXP, https://www.nxp.com/company/blog/how-zonal-e-e-architectures-with-ethernet-are-enabling-software-defined-vehicles:BL-HOW-ZONAL-EE-ARCHITECTURES.
8. WikiChip. “Tesla (Car Company)/FSD Chip.” WikiChip, https://en.wikichip.org/wiki/tesla_(car_company)/fsd_chip.
9. Mobileye. “EyeQ Chip.” Mobileye, https://www.mobileye.com/technology/eyeq-chip/.
10. Ziadeh, Bassam. “Driving Adoption of Advanced IC Packaging in Automotive Applications.” Presentation at IMAPS DPC, March 2023. General Motors, Fountain Hills AZ, March 16, 2023.
11. K Matthias Jung and Norbert Wehn. “Driving Against the Memory Wall: The Role of Memory for Autonomous Driving.” Fraunhofer IESE, Kaiserslautern, Germany, and Microelectronic Systems Design Research Group, University of Kaiserslautern, Kaiserslautern, Germany. Kluedo, https://kluedo.ub.rptu.de/frontdoor/deliver/index/docId/5286/file/_memory.pdf.
12. Micron. “Cinco de Play: Memory – Is That Critical to Autonomous Driving?” Micron, https://www.micron.com/about/blog/2017/october/cinco-play-memory-is-that-critical-to-autonomous-driving.
13. McKinsey & Company. “Advanced Chip Packaging: How Manufacturers Can Play to Win.” McKinsey & Company, https://www.mckinsey.com/industries/semiconductors/our-insights/advanced-chip-packaging-how-manufacturers-can-play-to-win.

The post Powering The Automotive Revolution: Advanced Packaging For Next-Generation Vehicle Computing appeared first on Semiconductor Engineering.

The need to mitigate climate change is driving a need to electrify our infrastructure, vehicles, and appliances, which can then be charged and powered by renewable energy sources. The most visible and impactful electrification is now under way for electric vehicles (EVs). Beyond the transition to electric engines, several new features and technologies are driving the electrification of vehicles. The number of sensors in a vehicle is skyrocketing, driven by autonomous driving and other safety features, while a modern software-defined vehicle (SDV) is electrifying everything from air-conditioned seats to self-parking technology.

An important technology for EVs and SDVs is power modules. These are super high-voltage devices that convert one form of electricity to another (e.g., AC to DC), which is necessary to convert the vehicle battery energy to a current that can run the vehicles electrical system, including the drive train. These modules demand the highest power loads and are rated at 1000s of voltages – and the design of power devices, which are the fundamental electronic component of the power modules, is crucial, as a bad design can lead to catastrophe events.

Power devices, much more than other types of electrical devices, are designed for specific applications. In comparison, logic transistors can be used in everything from toasters to smartphones. Not only does the architecture of power devices change at higher voltages, different power ratings, or higher switching frequencies as needed, but the material can change as well.

New power requirements need wide-band gap materials

To meet new and future power demands for EVs, electric infrastructure, and other novel electrical systems, wide-band gap (WBG) materials are being developed and introduced. Silicon carbide (SiC) IGBTs are now available and being deployed, while gallium arsenide (GaN) HEMTs are a promising technology that is in the development stage.

Power density vs. switching frequency of power devices based on different materials.

Continuing with our EV example, SiC inverters can generally increase the potential range by approximately 10%, even after accounting for other design considerations. In addition, increasing the drive train voltage from 400V range to 800V can reduce the charging speeds by half. These voltages are only possible to realize with wide-band gap materials like SiC-based power devices. Tesla introduced SiC MOSFETs into its Model S back in 2018. Since then, numerous automotive manufacturers have also adopted SiC in their EVs, including Hyundai and BMW, for example.

GaN still has many design hurdles to overcame to improve reliability and decrease cost – but if it can be made affordable, perhaps the next realization of EVs will allow for charging in seconds with ranges of thousands of miles.

Simulating power devices

Because of the huge number of design parameters, simulation is important in the design of power devices. One crucial part for device design is the calculation of the breakdown voltage – the voltage at which the device can essentially melt, or catch fire, but will never operate again. These simulations need to be highly physics-based and capture the mechanisms by which electrons can be released or absorbed by the crystal lattice of these materials. The increasing band gaps in WBG materials like SiC and GaN increase the breakdown voltage. In addition, these materials have a smaller effective electron mass (i.e., the mass of an electron in a material dictates how fast it will move in an electric field) – which makes the switching frequency in devices based on these WBG materials faster.

A critical area of all electronics design is variability and reliability. Device performance needs to be stable and last a long time. A key factor for variability and reliability is defects in the crystal lattice. These defects, or traps, act as charge centers that can drastically impact how well a device works. Simulation can also help to identify the types of traps, providing a mechanistic understanding of how the traps will impact the device physics. Recently, Synopsys issued a paper using first-principles quantum solutions to characterize specific traps in SiC with QuantumATK.

Going forward, wind energy, solar, home appliances, and even the electric grid itself are going to need new devices with different structures and materials. The future is extremely exciting for power devices, which can be found in our EVs and will soon power a huge range of applications across our society.

The post Enabling New Applications With SiC IGBT And GaN HEMT For Power Module Design appeared first on Semiconductor Engineering.

By Ann Keffer, Arun Gogineni, and James Kim

Cars deploying ADAS and AV features rely on complex digital and analog systems to perform critical real-time applications. The large number of faults that need to be tested in these modern automotive designs make performing safety verification using a single technology impractical.

Yet, developing an optimized safety methodology with specific fault lists automatically targeted for simulation, emulation and formal is challenging. Another challenge is consolidating fault resolution results from various fault injection runs for final metric computation.

The good news is that interoperability of fault injection engines, optimization techniques, and an automated flow can effectively reduce overall execution time to quickly close-the-loop from safety analysis to safety certification.

Figure 1 shows some of the optimization techniques in a safety flow. Advanced methodologies such as safety analysis for optimization and fault pruning, concurrent fault simulation, fault emulation, and formal based analysis can be deployed to validate the safety requirements for an automotive SoC.

Fig. 1: Fault list optimization techniques.

Proof of concept: an automotive SoC

Using an SoC level test case, we will demonstrate how this automated, multi-engine flow handles the large number of faults that need to be tested in advanced automotive designs. The SoC design we used in this test case had approximately three million gates. First, we used both simulation and emulation fault injection engines to efficiently complete the fault campaigns for final metrics. Then we performed formal analysis as part of finishing the overall fault injection.

Fig. 2: Automotive SoC top-level block diagram.

Figure 3 is a representation of the safety island block from figure 2. The color-coded areas show where simulation, emulation, and formal engines were used for fault injection and fault classification.

Fig. 3: Detailed safety island block diagram.

Fault injection using simulation was too time and resource consuming for the CPU core and cache memory blocks. Those blocks were targeted for fault injection with an emulation engine for efficiency. The CPU core is protected by a software test library (STL) and the cache memory is protected by ECC. The bus interface requires end-to-end protection where fault injection with simulation was determined to be efficient. The fault management unit was not part of this experiment. Fault injection for the fault management unit will be completed using formal technology as a next step.

Table 1 shows the register count for the blocks in the safety island.

Table 1: Block register count.

The fault lists generated for each of these blocks were optimized to focus on the safety critical nodes which have safety mechanisms/protection.

SafetyScope, a safety analysis tool, was run to create the fault lists for the FMs for both the Veloce Fault App (fault emulator) and the fault simulator and wrote the fault lists to the functional safety (FuSa) database.

For the CPU and cache memory blocks, the emulator inputs the synthesized blocks and fault injection/fault detection nets (FIN/FDN). Next, it executed the stimulus and captured the states of all the FDNs. The states were saved and used as a “gold” reference for comparison against fault inject runs. For each fault listed in the optimized fault list, the faulty behavior was emulated, and the FDNs were compared against the reference values generated during the golden run, and the results were classified and updated in the fault database with attributes.

Fig. 4: CPU cluster. (Source from https://developer.arm.com/Processors/Cortex-R52)

For each of the sub parts shown in the block diagram, we generated an optimized fault list using the analysis engine. The fault lists are saved into individual session in the FuSa database. We used the statistical random sampling on the overall faults to generate the random sample from the FuSa database.

Now let’s look at what happens when we take one random sample all the way through the fault injection using emulation. However, for this to completely close on the fault injection, we processed N samples.

Table 2: Detected faults by safety mechanisms.

Table 3 shows that the overall fault distribution for total faults is in line with the fault distribution of the random sampled faults. The table further captures the total detected faults of 3125 out of 4782 total faults. We were also able model the detected faults per sub part and provide an overall detected fault ratio of 65.35%. Based on the faults in the random sample and our coverage goal of 90%, we calculated that the margin of error (MOE) is ±1.19%.

Table 3: Results of fault injection in CPU and cache memory.

The total detected (observed + unobserved) 3125 faults provide a clear fault classification. The undetected observed also provide a clear classification for Residual faults. We did further analysis of undetected unobserved and not injected faults.

Table 4: Fault classification after fault injection.

We used many debug techniques to analyze the 616 Undetected Unobserved faults. First, we used formal analysis to check the cone of influence (COI) of these UU faults. The faults which were outside the COI were deemed safe, and there were five faults which were further dropped from analysis. For the faults which were inside the COI, we used engineering judgment with justification of various configurations like, ECC, timer, flash mem related etc. Finally, using formal and engineering judgment we were able to further classify 616 UU faults into safe faults and remaining UU faults into conservatively residual faults. We also reviewed the 79 residual faults and were able to classify 10 faults into safe faults. The not injected faults were also tested against the simulation model to check if any further stimulus is able to inject those faults. Since no stimulus was able to inject these faults, we decided to drop these faults from our consideration and against the margin of error accordingly. With this change our new MOE is ±1.293%.

In parallel, the fault simulator pulled the optimized fault lists for the failure modes of the bus block and ran fault simulations using stimulus from functional verification. The initial set of stimuli didn’t provide enough coverage, so higher quality stimuli (test vectors) were prepared, and additional fault campaigns were run on the new stimuli. All the fault classifications were written into the FuSa database. All runs were parallel and concurrent for overall efficiency and high performance.

Safety analysis using SafetyScope helped to provide more accuracy and reduce the iteration of fault simulation. CPU and cache mem after emulation on various tests resulted an overall SPFM of over 90% as shown in Table 5.

Table 5: Overall results.

At this time not all the tests for BUS block (end to end protection) doing the fault simulation had been completed. Table 6 shows the first initial test was able to resolve the 9.8% faults very quickly.

Table 6: Percentage of detected faults for BUS block by E2E SM.

We are integrating more tests which have high traffic on the BUS to mimic the runtime operation state of the SoC. The results of these independent fault injections (simulation and emulation) were combined for calculating the final metrics on the above blocks, with the results shown in Table 7.

Table 7: Final fault classification post analysis.

Conclusion

In this article we shared the details of a new functional safety methodology used in an SoC level automotive test case, and we showed how our methodology produces a scalable, efficient safety workflow using optimization techniques for fault injection using formal, simulation, and emulation verification engines. Performing safety analysis prior to running the fault injection was very critical and time saving. Therefore, the interoperability for using multiple engines and reading the results from a common FuSa database is necessary for a project of this scale.

For more information on this highly effective functional safety flow for ADAS and AV automotive designs, please download the Siemens EDA whitepaper Complex safety mechanisms require interoperability and automation for validation and metric closure.

Arun Gogineni is an engineering manager and architect for IC functional safety at Siemens EDA.

James Kim is a technical leader at Siemens EDA.

The post Interoperability And Automation Yield A Scalable And Efficient Safety Workflow appeared first on Semiconductor Engineering.

Silicon chips are central to today’s sophisticated advanced driver assistance systems, smart safety features, and immersive infotainment systems. Industry sources estimate that now there are over 1,000 integrated circuits (ICs), or chips, in an average ICE car, and twice as many in an average EV. Such a large amount of electronics translates into kilowatts of power being consumed – equivalent to a couple of dishwashers running continuously. For an ICE vehicle, this puts a lot of stress on the vehicle’s electrical and charging system, leading automotive manufacturers to consider moving to 48V systems (vs. today’s mainstream 12V systems). These 48V systems reduce the current levels in the vehicle’s wiring, enabling the use of lower cost smaller-gauge wire, as well as delivering higher reliability. For EVs, higher energy efficiency of on-board electronics translates directly into longer range – the primary consideration of many EV buyers (second only to price). Driver assistance and safety features often employ redundant component techniques to ensure reliability, further increasing vehicle energy consumption. Lack of energy efficiency for an EV also means more frequent charging, further stressing the power grid and producing a detrimental effect on the environment. All these considerations necessitate the need for a comprehensive energy-efficient design methodology for automotive ICs.

What’s driving demand for compute power in cars?

Classification and processing of massive amounts of data from multiple sources in automotive applications – video, audio, radar, lidar – results in a high degree of complexity in automotive ICs as software algorithms require large amounts of compute power. Hardware architectural decisions, and even hardware-software partitioning, must be done with energy efficiency in mind. There is a plethora of tradeoffs at this stage:

• Flexibility of a general-purpose CPU-based architecture vs. efficiency of a dedicated digital signal processor (DSP) vs. a hardware accelerator
• Memory sub-system design: how much is required, how it will be partitioned, how much precision is really needed, just to name a few considerations

In order to enable reliable decisions, architects must have access to a system that models, in a robust manner, power, performance, and area (PPA) characteristics of the hardware, as well as use cases. The idea is to eliminate error-prone estimates and guesswork.

To improve energy efficiency, automotive IC designers also must adopt many of the power reduction techniques traditionally used by architects and engineers in the low-power application space (e.g. mobile or handheld devices), such as power domain shutoff, voltage and frequency scaling, and effective clock and data gating. These techniques can be best evaluated at the hardware design level (register transfer level, or RTL) – but with the realistic system workload. As a system workload – either a boot sequence or an application – is millions of clock cycles long, only an emulation-based solution delivers a practical turnaround time (TAT) for power analysis at this stage. This power analysis can reveal intervals of wasted power – power consumption bugs – whether due to active clocks when the data stream is not active, redundant memory access when the address for the read operation doesn’t change for many clock cycles (and/or when the address and data input don’t change for the write operation over many cycles), or unnecessary data toggles while clocks are gated off.

To cope with the huge amount of data and the requirement to process that data in real time (or near real time), automotive designers employ artificial intelligence (AI) algorithms, both in software and in hardware. Millions of multiply-accumulate (MAC) operations per second and other arithmetic-intensive computations to process these algorithms give rise to a significant amount of wasted power due to glitches – multiple signal transitions per clock cycle. At the RTL stage, with the advanced RTL power analysis tools available today, it is possible to measure the amount of wasted power due to glitches as well as to identify glitch sources. Equipped with this information, an RTL design engineer can modify their RTL source code to lower the glitch activity, reduce the size of the downstream logic, or both, to reduce power.

Working together with the RTL design engineer is another critical persona – the verification engineer. In order to verify the functional behavior of the design, the verification engineer is no longer dealing just with the RTL source: they also have to verify the proper functionality of the global power reduction techniques such as power shutoff and voltage/frequency scaling. Doing so requires a holistic approach that leverages a comprehensive description of power intent, such as the Unified Power Format (UPF). All verification technologies – static, formal, emulation, and simulation – can then correctly interpret this power intent to form an effective verification methodology.

Power intent also carries through to the implementation part of the flow, as well as signoff. During the implementation process, power can be further optimized through physical design techniques while conforming to timing and area constraints. Highly accurate power signoff is then used to check conformance to power specifications before tape-out.

Design and verification flow for more energy-efficient automotive SoCs

Synopsys delivers a complete end-to-end solution that allows IC architects and designers to drive energy efficiency in automotive designs. This solution spans the entire design flow from architecture to RTL design and verification, to emulation-driven power analysis, to implementation and, ultimately, to power signoff. Automotive IC design teams can now put in place a rigorous methodology that enables intelligent architectural decisions, RTL power analysis with consistent accuracy, power-aware physical design, and foundry-certified power signoff.

The post Maximizing Energy Efficiency For Automotive Chips appeared first on Semiconductor Engineering.