Increasingly tight tolerances and rigorous demands for quality are forcing chipmakers and equipment manufacturers to ferret out minor process variances, which can create significant anomalies in device behavior and render a device non-functional.

In the past, many of these variances were ignored. But for a growing number of applications, that’s no longer possible. Even minor fluctuations in deposition rates during a chemical vapor deposition (CVD) process, for example, can lead to inconsistencies in layer uniformity, which can impact the electrical isolation properties essential for reliable circuit operation. Similarly, slight variations in a photolithography step can cause alignment issues between layers, leading to shorts or open circuits in the final device.

Some of these variances can be attributed to process error, but more frequently they stem from process drift — the gradual deviation of process parameters from their set points. Drift can occur in any of the hundreds of process steps involved in manufacturing a single wafer, subtly altering the electrical properties of chips and leading to functional and reliability issues. In highly complex and sensitive ICs, even the slightest deviations can cause defects in the end product.

“All fabs already know drift. They understand drift. They would just like a better way to deal with drift,” said David Park, vice president of marketing at Tignis. “It doesn’t matter whether it’s lithography, CMP (chemical mechanical polishing), CVD or PVD (chemical/physical vapor deposition), they’re all going to have drift. And it’s all going to happen at various rates because they are different process steps.”

At advanced nodes and in dense advanced packages, where a nanometer can be critical, controlling process drift is vital for maintaining high yield and ensuring profitability. By rigorously monitoring and correcting for drift, engineers can ensure that production consistently meets quality standards, thereby maximizing yield and minimizing waste.

“Monitoring and controlling hundreds of thousands of sensors in a typical fab requires the ability to handle petabytes of real-time data from a large variety of tools,” said Vivek Jain, principal product manager, smart manufacturing at Synopsys. “Fabs can only control parameters or behaviors they can measure and analyze. They use statistical analysis and error budget breakdowns to define upper control limits (UCLs) and lower control limits (LCLs) to monitor the stability of measured process parameters and behaviors.”

Dialing in legacy fabs
In legacy fabs — primarily 200mm — most of the chips use 180nm or older process technology, so process drift does not need to be as precisely monitored as in the more advanced 300mm counterparts. Nonetheless, significant divergence can lead to disparities in device performance and reliability, creating a cascade of operational challenges.

Manufacturers operating at older technology nodes might lack the sophisticated, real-time monitoring and control methods that are standard in cutting-edge fabs. While the latter have embraced ML to predict and correct for drift, many legacy operations still rely heavily on periodic manual checks and adjustments. Thus, the management of process drift in these settings is reactive rather than proactive, making changes after problems are detected rather than preventing them.

“There is a separation between 300-millimeter and 200-millimeter fabs,” said Park. “The 300-millimeter guys are all doing some version of machine learning. Sometimes it’s called advanced process control, and sometimes it’s actually AI-powered process control. For some of the 200-millimeter fabs with more mature process nodes, they basically have a recipe they set and a bunch of technicians looking at machines and looking at the CDs. When the drift happens, they go through their process recipe and manually adjust for the out-of-control processes, and that’s just what they’ve always done. It works for them.”

For these older fabs, however, the repercussions of process drift can be substantial. Minor deviations in process parameters, such as temperature or pressure during the deposition or etching phases, gradually can lead to changes in the physical structure of the semiconductor devices. Over time, these minute alterations can compound, resulting in layers of materials that deviate from their intended characteristics. Such deviations affect critical dimensions and ultimately can compromise the electrical performance of the chip, leading to slower processing speeds, higher power consumption, or outright device failure.

The reliability equation is equally impacted by process drift. Chips are expected to operate consistently over extended periods, often under a range of environmental conditions. However, when process-induced variability can weaken the device’s resilience, precipitating early wear-out mechanisms and reducing its lifetime. In situations where dependability is non-negotiable, such as in automotive or medical applications, those variations can have dire consequences.

But with hundreds of process steps for a typical IC, eliminating all variability in fabs is simply not feasible.

“Process drift is never going to not happen, because the processes are going to have some sort of side effect,” Park said. “The machines go out of spec and things like pumps and valves and all sorts of things need to be replaced. You’re still going to have preventive maintenance (PM). But if the critical dimensions are being managed correctly, which is typically what triggers the drift, you can go a longer period of time between cleanings or the scheduled PMs and get more capacity.”

Process drift pitfalls
Managing process drift in semiconductor manufacturing presents several complex challenges. Hysteresis, for example, is a phenomenon where the output of a process varies not solely because of current input conditions, but also based on the history of the states through which the process already has passed. This memory effect can significantly complicate precision control, as materials and equipment might not reset to a baseline state after each operational cycle. Consequently, adjustments that were effective in previous cycles may not yield the same outcomes due to accumulated discrepancies.

One common cause of hysteresis is thermal cycling, where repeated heating and cooling create mechanical stresses. Those stresses can be additive, releasing inconsistently based on temperature history.  That, in turn, can lead to permanent changes in the output of a circuit, such as a voltage reference, which affects its precision and stability.

In many field-effect transistors (FETs), hysteresis also can occur due to charge trapping. This happens when charges are captured in ‘trap states’ within the semiconductor material or at the interface with another material, such as an oxide layer. The trapped charges then can modulate the threshold voltage of the device over time and under different electrical biases, potentially leading to operational instability and variability in device performance.

Human factors also play a critical role in process drift, with errors stemming from incorrect settings adjustments, mishandling of materials, misinterpretation of operational data, or delayed responses to process anomalies. Such errors, though often minor, can lead to substantial variations in manufacturing processes, impacting the consistency and reliability of semiconductor devices.

“Once in production, the biggest source of variability is human error or inconsistency during maintenance,” said Russell Dover, general manager of service product line at Lam Research. “Wet clean optimization (WCO) and machine learning through equipment intelligence solutions can help address this.”

The integration of new equipment into existing production lines introduces additional complexities. New machinery often features increased speed, throughput, and tighter tolerances, but it must be integrated thoughtfully to maintain the stringent specifications required by existing product lines. This is primarily because the specifications and performance metrics of legacy chips have been long established and are deeply integrated into various applications with pre-existing datasheets.

“From an equipment supplier perspective, we focus on tool matching,” said Dover. “That includes manufacturing and installing tools to be identical within specification, ensuring they are set up and running identically — and then bringing to bear systems, tooling, software and domain knowledge to ensure they are maintained and remain as identical as possible.”

The inherent variability of new equipment, even those with advanced capabilities, requires careful calibration and standardization.

“Some equipment, like transmission electron microscopes, are incredibly powerful,” said Jian-Min Zuo, a materials science and engineering professor at the University of Illinois’ Grainger College of Engineering. “But they are also very finicky, depending on how you tune the machine. How you set it up under specific conditions may vary slightly every time. So there are a number of things that can be done when you try to standardize those procedures, and also standardize the equipment. One example is to generate a curate, like a certain type of test case, where you can collect data from different settings and make sure you’re taking into account the variability in the instruments.”

Process drift solutions
As semiconductor manufacturers grapple with the complexities of process drift, a diverse array of strategies and tools has emerged to address the problem. Advanced process control (APC) systems equipped with real-time monitoring capabilities can extract patterns and predictive insights from massive data sets gathered from various sensors throughout the manufacturing process.

By understanding the relationships between different process variables, APC can predict potential deviations before they result in defects. This predictive capability enables the system to make autonomous adjustments to process parameters in real-time, ensuring that each process step remains within the defined control limits. Essentially, APC acts as a dynamic feedback mechanism that continuously fine-tunes the production process.

Fig. 1: Reduced process drift with AI/ML advanced process control. Source: Tignis

While APC proactively manages and optimizes the process to prevent deviations, fault detection and classification (FDC) reacts to deviations by detecting and classifying any faults that still occur.

FDC data serves as an advanced early-warning system. This system monitors the myriad parameters and signals during the chip fabrication process, rapidly detecting any variances that could indicate a malfunction or defect in the production line. The classification component of FDC is particularly crucial, as it does more than just flag potential issues. It categorizes each detected fault based on its characteristics and probable causes, vastly simplifying the trouble-shooting process. This allows engineers to swiftly pinpoint the type of intervention needed, whether it’s recalibrating instruments, altering processing recipes, or conducting maintenance repairs.

Statistical process control (SPC) is primarily focused on monitoring and controlling process variations using statistical methods to ensure the process operates efficiently and produces output that meets quality standards. SPC involves plotting data in real-time against control limits on control charts, which are statistically determined to represent the expected normal process behavior. When process measurements stray outside these control limits, it signals that the process may be out of control due to special causes of variation, requiring investigation and correction. SPC is inherently proactive and preventive, aiming to detect potential problems before they result in product defects.

“Statistical process control (SPC) has been a fundamental methodology for the semiconductor industry almost from its very foundation, as there are two core factors supporting the need,” said Dover. “The first is the need for consistent quality, meaning every product needs to be as near identical as possible, and second, the very high manufacturing volume of chips produced creates an excellent workspace for statistical techniques.”

While SPC, FDC, and APC might seem to serve different purposes, they are deeply interconnected. SPC provides the baseline by monitoring process stability and quality over time, setting the stage for effective process control. FDC complements SPC by providing the tools to quickly detect and address anomalies and faults that occur despite the preventive measures put in place by SPC. APC takes insights from both SPC and FDC to adjust process parameters proactively, not just to correct deviations but also to optimize process performance continually.

Despite their benefits, integrating SPC, FDC and APC systems into existing semiconductor manufacturing environments can pose challenges. These systems require extensive configuration and tuning to adapt to specific manufacturing conditions and to interface effectively with other process control systems. Additionally, the success of these systems depends on the quality and granularity of the data they receive, necessitating high-fidelity sensors and a robust data management infrastructure.

“For SPC to be effective you need tight control limits,” adds Dover. “A common trap in the world of SPC is to keep adding control charts (by adding new signals or statistics) during a process ramp, or maybe inheriting old practices from prior nodes without validating their relevance. The result can be millions of control charts running in parallel. It is not a stretch to state that if you are managing a million control charts you are not really controlling much, as it is humanly impossible to synthesize and react to a million control charts on a daily basis.”

This is where AI/ML becomes invaluable, because it can monitor the performance and sustainability of the new equipment more efficiently than traditional methods. By analyzing data from the new machinery, AI/ML can confirm observations, such as reduced accumulation, allowing for adjustments to preventive maintenance schedules that differ from older equipment. This capability not only helps in maintaining the new equipment more effectively but also in optimizing the manufacturing process to take full advantage of the technological upgrades.

AI/ML also facilitate a smoother transition when integrating new equipment, particularly in scenarios involving ‘copy exact’ processes where the goal is to replicate production conditions across different equipment setups. AI and ML can analyze the specific outputs and performance variations of the new equipment compared to the established systems, reducing the time and effort required to achieve optimal settings while ensuring that the new machinery enhances production without compromising the quality and reliability of the legacy chips being produced.

AI/ML
Being more proactive in identifying drift and adjusting parameters in real-time is a necessity. With a very accurate model of the process, you can tune your recipe to minimize that variability and improve both quality and yield.

“The ability to quickly visualize a month’s worth of data in seconds, and be able to look at windows of time, is a huge cost savings because it’s a lot more involved to get data for the technicians or their process engineers to try and figure out what’s wrong,” said Park. “AI/ML has a twofold effect, where you have fewer false alarms, and just fewer alarms in general. So you’re not wasting time looking at things that you shouldn’t have to look at in the first place. But when you do find issues, AI/ML can help you get to the root cause in the diagnostics associated with that much more quickly.”

When there is a real alert, AI/ML offers the ability to correlate multiple parameters and inputs that are driving that alert.

“Traditional process control systems monitor each parameter separately or perform multivariate analysis for key parameters that require significant effort from fab engineers,” adds Jain. “With the amount of fab data scaling exponentially, it is becoming humanly impossible to extract all the actionable insights from the data. Machine learning and artificial intelligence can handle big data generated within a fab to provide effective process control with minimal oversight.”

AI/ML also can look for more other ways of predicting when the drift is going to take your process out of specification. Those correlations can be bivariate and multivariate, as well as univariate. And a machine learning engine that is able to sift through tremendous amounts of data and a larger number of variables than most humans also can turn up some interesting correlations.

“Another benefit of AI/ML is troubleshooting when something does trigger an alarm or alert,” adds Park. “You’ve got SPC and FDC that people are using, and a lot of them have false positives, or false alerts. In some cases, it’s as high as 40% of the alerts that you get are not relevant for what you’re doing. This is where AI/ML becomes vital. It’s never going to take false alerts to zero, but it can significantly reduce the amount of false alerts that you have.”

Engaging with these modern drift solutions, such as AI/ML-based systems, is not mere adherence to industry trends but an essential step towards sustainable semiconductor production. Going beyond the mere mitigation of process drift, these technologies empower manufacturers to optimize operations and maintain the consistency of critical dimensions, allowed by the intelligent analysis of extensive data and automation of complex control processes.

Conclusion
Monitoring process drift is essential for maintaining quality of the device being manufactured, but it also can ensure that the entire fabrication lifecycle operates at peak efficiency. Detecting and managing process drift is a significant challenge in volume production because these variables can be subtle and may compound over time. This makes identifying the root cause of any drift difficult, particularly when measurements are only taken at the end of the production process.

Combating these challenges requires a vigilant approach to process control, regular equipment servicing, and the implementation of AI/ML algorithms that can assist in predicting and correcting for drift. In addition, fostering a culture of continuous improvement and technological adaptation is crucial. Manufacturers must embrace a mindset that prioritizes not only reactive measures, but also proactive strategies to anticipate and mitigate process drift before it affects the production line. This includes training personnel to handle new technologies effectively and to understand the dynamics of process control deeply. Such education enables staff to better recognize early signs of drift and respond swiftly and accurately.

Moreover, the integration of comprehensive data analytics platforms can revolutionize how fabs monitor and analyze the vast amounts of data they generate. These platforms can aggregate data from multiple sources, providing a holistic view of the manufacturing process that is not possible with isolated measurements. With these insights, engineers can refine their process models, enhance predictive maintenance schedules, and optimize the entire production flow to reduce waste and improve yields.

Related Reading
Tackling Variability With AI-Based Process Control
How AI in advanced process control reduces equipment variability and corrects for process drift.

The post Predicting And Preventing Process Drift appeared first on Semiconductor Engineering.

A new technical paper titled “Combining Power and Arithmetic Optimization via Datapath Rewriting” was published by researchers at Intel Corporation and Imperial College London.

Abstract:
“Industrial datapath designers consider dynamic power consumption to be a key metric. Arithmetic circuits contribute a major component of total chip power consumption and are therefore a common target for power optimization. While arithmetic circuit area and dynamic power consumption are often correlated, there is also a tradeoff to consider, as additional gates can be added to explicitly reduce arithmetic circuit activity and hence reduce power consumption. In this work, we consider two forms of power optimization and their interaction: circuit area reduction via arithmetic optimization, and the elimination of redundant computations using both data and clock gating. By encoding both these classes of optimization as local rewrites of expressions, our tool flow can simultaneously explore them, uncovering new opportunities for power saving through arithmetic rewrites using the e-graph data structure. Since power consumption is highly dependent upon the workload performed by the circuit, our tool flow facilitates a data dependent design paradigm, where an implementation is automatically tailored to particular contexts of data activity. We develop an automated RTL to RTL optimization framework, ROVER, that takes circuit input stimuli and generates power-efficient architectures. We evaluate the effectiveness on both open-source arithmetic benchmarks and benchmarks derived from Intel production examples. The tool is able to reduce the total power consumption by up to 33.9%.”

Find the technical paper here. Published April 2024.

Samuel Coward, Theo Drane, Emiliano Morini, George Constantinides; arXiv:2404.12336v1.

The post Merging Power and Arithmetic Optimization Via Datapath Rewriting (Intel, Imperial College London) appeared first on Semiconductor Engineering.

SK hynix and TSMC plan to collaborate on HBM4 development and next-generation packaging technology, with plans to mass produce HBM4 chips in 2026. The agreement is an early indicator for just how competitive, and potentially lucrative, the HBM market is becoming. SK hynix said the collaboration will enable breakthroughs in memory performance with increased density of the memory controller at the base of the HBM stack.

Intel assembled the industry’s first high-NA EUV lithography system. “Compared to 0.33NA EUV, high-NA EUV (or 0.55NA EUV) can deliver higher imaging contrast for similar features, which enables less light per exposure, thereby reducing the time required to print each layer and increasing wafer output,” Intel said.

Fig. 1: Bigger iron — Intel’s brand new high-NA EUV machinery. Source: Intel

Samsung is slated to receive $6.4 billion in CHIPS ACT funding from the U.S. Department of Commerce (DoC) as part of a $40 billion expansion of its Austin, Texas, manufacturing facility, along with an R&D fab, a pair of leading-edge logic fabs, and an advanced packaging plant in nearby Taylor, Texas.

Micron and the U.S. government next week will announce $6.1 billion in CHIPS Act funding for the development of advanced memory chips in New York and Idaho, according to AP News.

Cadence unveiled its Palladium Z3 Emulation and Protium X3 FPGA Prototyping systems, targeted at multi-billion-gate designs with 2X increase in capacity and a 1.5X performance increase compared to previous-generation systems. Cadence also teamed up with MemVerge to enable seamless support for AWS Spot instances for long-running high-memory EDA jobs, and extended its hybrid cloud environment solutions through a collaboration with NetApp.

Fig. 2: At CadenceLive Silicon Valley, NVIDIA CEO Jensen Huang (r.) discussed accelerated computing and generative AI with Cadence CEO Anirudh Devgan. Source: Semiconductor Engineering

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Global

After Taiwan’s recent 7.2 magnitude earthquake, TSMC reached more the 70% tool recovery in its fabs within the first 10 hours and full recovery by the end of the third day, according to this week’s earnings call. Some wafers in process were scrapped but the company expects the lost production to be recovered in the second quarter.  Also in the call, TSMC said they expect their “customers to share some of the higher cost” of the overseas fabs and higher electricity costs.

Advantest‘s regional headquarters in Taiwan donated $2.2 million New Taiwan dollars ($680,000 US) for aid to victims and reconstruction efforts related to the Taiwan earthquake that struck on April 3.

Japan’s exports grew by more than 7% YoY in March, driven by an 11.3% increase in shipments of electronics and semiconductor manufacturing equipment, much of it to China, according to NikkeiAsia.

China‘s IC output grew 40% in the first quarter, primarily driven by EVs and smartphones, according to the South China Morning Post.

In the U.S., the Biden Administration released a notice of funding opportunity of $50 million targeted at small businesses pursuing advances in metrology research and technology. Also, the U.S. Department of Energy announced a $33 million funding opportunity for smart manufacturing technologies.

Germany‘s Fraunhofer IIS launched its On-Board Processor (FOBP) for the German Space Agency’s Heinrich Hertz communication satellite. FOBP can be controlled and reprogrammed from Earth and will be used to investigate creation of hybrid communication networks.

Markets and Money

RISC-V startup Rivos raised more than $250 million in capital investments to tape out its first power-optimized chips for data analytics and generative AI applications.

Silvaco filed to go public on Nasdaq. The company also received a $5 million convertible note investment from Microchip.

Microchip acquired Neuronix AI Labs to provide AI-enabled FPGA solutions for large-scale, high-performance edge applications.

The advanced packaging market saw a modest 4% increase in revenues in Q4 2023 versus the previous quarter, with a projected decline of 13% QoQ in the first quarter of 2024, reports Yole. Overall, the market is expected to increase from $38 billion in 2023 to $69.5 billion in 2029 with a CAGR of 10.7%.

TSMC’s CoWoS total capacity will increase by 150% in 2024 due to demand for NVIDIA’s Blackwell Platform, reports TrendForce.

ASML saw a nearly 40% drop in new litho equipment sales QoQ in Q1 2024 and a 61% drop in net bookings as manufacturers reduced investments in new capital equipment during the recent semiconductor market slump.

Global PC shipments rose about 3% YoY in Q1 2024, and that same growth is expected for full year 2024, reports Counterpoint. Manufacturers are predicted to promote AI PCs as semiconductor companies prepare to launch SoCs featuring higher TOPS.

The GenAI smartphone market share is predicted to reach 11% by 2024 and 43% by 2027, reports Counterpoint. Samsung likely will lead in 2024, but Apple may overtake it in 2025.

The RF GaN market is expected to exceed $2 billion by 2029, fueled by the defense and telecom infrastructure sectors, reports Yole.

In-Depth

Semiconductor Engineering published its Manufacturing, Packaging & Materials newsletter this week. Top articles include:

• Electromigration Concerns Grow In Advanced Packages
• What Works Best For Chiplets
• Enabling Advanced Devices With Atomic Layer Processes

Plus, check out these new stories and tech talks:

• Architecting Chips For High-Performance Computing
• Future-Proofing Automotive V2X
• Secure Movement Of Data In Test
• Challenges With Chiplets and Power Delivery

Security

In security research:

• Seoul National University, Sandia National Laboratories, Texas A&M University, and Applied Materials demonstrated a memristor crossbar architecture for encryption and decryption.
• Robert Bosch, Forschungszentrum Julich, and Newcastle University investigated techniques for error detection and correction in in-memory computing.
• The University of Florida introduced an automated framework that can help identify security assets for a design at the register-transfer level (RTL).

DARPA conducted successful in-air tests of AI flying an F-16 autonomously versus a human-piloted F-16 in visual-range combat scenarios.

The National Security Agency’s Artificial Intelligence Security Center (NSA AISC) published joint guidance on deploying AI systems securely with the Cybersecurity and Infrastructure Security Agency (CISA), the Federal Bureau of Investigation (FBI), and international partners. CISA also issued other alerts.

Products and Standards

Samsung uncorked LPDDR5X DRAM built on a 12nm process that supports up to 10.7 Gbps and expands the single package capacity of mobile DRAM up to 32 GB.

Keysight revealed its next-generation RF circuit simulation tool that supports multi-physics co-design of circuit, electromagnetic, and electrothermal simulations across Cadence, Synopsys, and Keysight platforms.

Renesas released its FemtoClock family of ultra-low jitter clock generators and jitter attenuators with 8 and 12 outputs, enabling clock tree designs for high-speed interconnect systems in telecom and data center switches, routers, medical imaging, and more.

Movellus expanded its droop response solutions with Aeonic Generate AWM3, which responds to voltage droops within 1 to 2 clock cycles while providing enhanced observability for droop profiling and enabling fine-grained dynamic frequency scaling.

Efabless announced the second version of its Python-based open-source EDA software for construction of customizable flows using proprietary or open-source tools.

Faraday Technology licensed Arm’s Cortex-A720AE IP to use in the development of AI-enabled vehicle ASICs. Also, Untether AI teamed up with Arm to enable its inference acceleration technology to be implemented alongside the latest-generation Automotive Enhanced technology from Arm for ADAS and autonomous vehicle applications.

FOXESS used Infineon’s 1,200V CoolSiC MOSFETs and EiceDRIVER gate drivers for industrial energy storage applications, aiming to promote green energy.

Emotors adopted Siemens’ Simcenter solutions for NVH testing of next-gen automotive e-drives.

SiTime debuted a family of clock generators for AI datacenter applications with clock, oscillator, and resonator in an integrated chip.

JEDEC published the JESD79-5C DDR5 SDRAM standard, which includes a DRAM data integrity improvement called Per-Row Activation Counting (PRAC) that precisely counts DRAM activations on a wordline granularity and alerts the system to pause traffic and designate time for mitigation measures when an excessive number of activations are detected.

The LoRa Alliance launched its roadmap for the development of the LoRaWAN open standard for IoT communications, referring to long-range radio (LoRa) low-power wide-area networks (LPWANs).

Education and Workforce

Texas A&M introduced a new Master of Science program for microelectronics and semiconductors, which will begin in fall 2025.

The Cornell NanoScale Science and Technology Facility (CNF) is partnering with Tompkins Cortland Community College and Penn State to offer a free Microelectronics and Nanomanufacturing Certificate Program to veterans and their dependents.

Eindhoven University of Technology (TU/e) has more than 700 researchers and 25 research group focused on the chip industry, but the number is projected to grow significantly due to the Dutch government’s recent investment.

Research

Intel announced a large-scale neuromorphic system based on its Loihi 2 processor. Initially deployed at Sandia National Laboratories, it aims to support research for future brain-inspired AI. Intel is also collaborating with Seekr on next-gen LLM and foundation models.

Los Alamos National Lab, HPE, and NVIDIA collaborated on the design and installation of Venado, the Lab’s new supercomputer. “Venado adds to our cutting-edge supercomputing that advances national security and basic research, and it will accelerate how we integrate artificial intelligence into meeting those challenges,” said Thom Mason, director of Los Alamos National Laboratory in a release.

Penn State is partnering with Morgan Advanced Materials on a five-year, multi-million-dollar research project to advance silicon carbide (SiC) technology. Morgan will become a founding member of the Penn State Silicon Carbide Innovation Alliance. Also, Coherent secured CHIPS Act funding of $15 million for research into high-voltage, high-power silicon carbide and single-crystal diamond semiconductors.

Oak Ridge National Laboratory (ORNL) researchers found a more efficient way to extract lithium from waste liquids leached from mining sites, oil fields, and used batteries.

Quantum

Quantinuum said it reached an inherent 99.9% 2-qubit gate fidelity in its commercial quantum computer, a point at which quantum error correction protocols can be used to greatly reduce error rates.

D-Wave Quantum uncorked a fast-anneal feature to speed up computations on its quantum processing units, which reduces the impact of external disturbances.

MIT researchers outlined a new conceptual model for a quantum computer that aims to make writing code for them easier.

SLAC National Accelerator Laboratory, Stanford University, Max Planck Institute of Quantum Optics, Ludwig-Maximilians-Universitat Munich, and Instituto de Ciencia de Materiales de Madrid researchers proposed a method that harnesses the structure of light to tweak the properties of quantum materials.

Events

Find upcoming chip industry events here, including:

Upcoming webinars are here.

Further Reading

Read the latest special reports and top stories, or check out the latest newsletters:

Systems and Design
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Test, Measurement and Analytics
Manufacturing, Packaging and Materials
Automotive, Security and Pervasive Computing

The post Chip Industry Week In Review appeared first on Semiconductor Engineering.

The incessant demand for more speed in chips requires forcing more energy through ever-smaller devices, increasing current density and threatening long-term chip reliability. While this problem is well understood, it’s becoming more difficult to contain in leading-edge designs.

Of particular concern is electromigration, which is becoming more troublesome in advanced packages with multiple chiplets, where various bonding and interconnect schemes create abrupt changes in materials and geometries. For example, electrons may travel from a copper trace to a solder bump of SAC (tin-silver-copper), then to an underbump metal based on nickel, and finally to an interposer copper pad. That, in turn, can cause atoms to shift, resulting in failures in solder joints or in copper redistribution layers in high-density fan-out packages.

“From an electromigration perspective, advanced packaging causes increased packaging density, reduced packaging size, and the dimensions of interconnects to shrink, so the current density is now in close proximity to the maximum current density limit per EM design rules,” said Dermott Lynch, director of technical product management in Synopsys‘ EDA Group.

Any additional stresses the package may be subjected to during assembly and use, whether mechanical or thermal, also can help induce or accelerate electromigration. “Electromigration, in general, gets worse due to temperature and stress, both of which advanced packaging increases,” said Lynch. “Electromigration is also cumulative, so essentially it integrates all the temperature highs and stress over the lifetime until an interconnect breaks down or shorts. Larger processing temperature and operation temperature will make it worse, but it also depends on time under that temperature.”

In fact, managing thermal pathways is perhaps the greatest challenge associated the movement toward the ultimate package, a 3D-IC. “Electromigration is very temperature-sensitive,” said Marc Swinnen, director of product marketing in Ansys’ Semiconductor Division. “Depending on your thermal map, your power integrity will have to adapt to the local temperature profile that you have. So when you look at a chip, you can calculate how much power the chip is putting out, but you cannot tell how hot the chip will get because ‘it depends.’ Is it sitting on a cold plate or sitting in the sun in the Sahara? System concerns come in, and multi-physics modeling is important to understanding these co-dependent effects.”

Thermal engineering also means moving heat away from the most vulnerable points of failure, such as solder bumps. “Effective thermal management is essential for bump reliability,” said Curtis Zwenger, vice president of engineering and technical marketing at Amkor. “Engineers are incorporating thermal enhancement techniques, such as the use of thermal interface materials and advanced heat dissipation solutions, to ensure that bumps are not subjected to excessive temperature-related stresses.”

Zwenger noted that engineers are looking into new materials, while optimizing the use of existing materials to minimize the possibility of electromigration. “Semiconductor packaging engineers are implementing a range of measures to enhance bump reliability and maximize bump yield. These strategies include new materials for solder bumps and underbump metallization, optimizing bump size, pitch and shape for reliability, advanced process control methods to control variability and maximize yield, and simulating and modeling reliability.”

What is electromigration?
Electromigration is the mass transport of metal atoms caused by the electron wind from current flowing through a conductor, typically copper. When current density is high enough, metal will diffuse in the direction of current flow, creating tiny hillocks downstream and leaving behind vacancies or voids. With enough electromigration, failures occur due to severe line thinning, causing opens, or due to hillocks that bridge adjacent lines, causing short circuits.

Electromigration is a diffusion-controlled mechanism that can take three forms — bulk, grain boundary, or surface diffusion, depending on the metal. Aluminum migrates by grain boundary diffusion whereas copper migrates on the surface or at its grain boundaries.

For most of the semiconductor industry’s history, electromigration was primarily an on-chip concern, but on-chip EM is largely under control by reliability engineers. But with the scaling and rapid developments in advanced packaging — implementing TSVs, fan-out packaging with redistribution layers, and copper pillar bumps — electromigration has emerged as a major threat at the package level. Current flowing through the solder bump causes joule heating, and heat from other parts of the package may also dissipate through the solder bumps. EM can become an issue for solder joint connections between chip and interposer, or chip and PCB, as well as in RDLs. Solder joint failures typically manifest as voids or cracks.

Fig. 1: Electromigration can create short circuits between two interconnects through the development of hillocks, or an open circuit through the creation of voids in interconnect. Source: Ansys

Electromigration progresses more quickly at higher temperatures, at higher currents, under greater mechanical stress and in the presence of defects or impurities in the metal. Black’s equation describes an interconnect’s mean time-to-failure with respect to its temperature, current density and the activation energy needed to dislodge a metal atom as:

J is the current density, k is Boltzmann’s constant, T is temperature, Ea is the activation energy, and N is a scaling factor that depends on the metal’s properties. Black’s equation is useful because it easily shows how shorter, wider interconnects will tend to have longer MTTF. In addition, electromigration time-to-failure very strongly depends on the interconnect’s temperature. That temperature is primarily the result of the chip’s environmental temperature, self-heating of the conductor caused by current flow, the heat from neighboring interconnects or transistors, and the thermal conductivity of the surrounding material.

It is also important to note that electromigration is a runaway process. As current density and/or temperature increases, electromigration increases, which raises current density, causing more metal to migrate in a destructive feedback loop.

EM failure modes and allowable current density
In the case of copper redistribution layers in polyimide material, as current flows through the RDL, heat accumulates in the conductor due to Joule heating generation, which can degrade performance. As the required current density and Joule heating temperature is increasing in the fine-line Cu RDL structures (<5nm lines and spaces), self-heating is considered a key factor in the reliability of high-density fan out packages.

JiHye Kwon, senior manager of R&D at Amkor, recently used EM testing and Black’s equation to determine the electromigration failure mechanisms for a given RDL stack and high-density fan-out package with 2µm or 10µm wide RDL layers, 1,000µm long. [1]

High density fan-out is an emerging technology, as it features more aggressive scaling than wafer level fan-out packages. The three layers of copper RDL (3µm thick with Ta/Cu seed) were fabricated followed by polyimide fill, copper pillar deposition, die attach, and overmold. Kwon’s team tested both 2 and 10µm RDL at different current densities and temperatures until resistance increased by 100% (EM failure), but the maximum allowed current density corresponded with a 20% resistance increase. The failure modes occurred in two stages, first by void nucleation and growth and second with copper reduction and oxidation. The study yielded Ea and current density exponent values that can be useful in future designs of RDLs.

Meanwhile, a team of researchers from ASE recently demonstrated how susceptibility to electromigration is determined on copper pillar interconnects in flip chip quad flat no-lead (FCQFN) for high-power automotive applications. The multi-layered copper pillar bumps with a Cu/Ni/Sn1.8Ag configuration were bonded to a silver-plated copper leadframe and tested under extreme EM conditions of 10 kA/cm² current density and temperatures of 150°C, 160°C and 180°C, while taking in-situ resistance measurements. [2] The EM failures corresponded with rapid rises in electrical resistance that corresponded with the formation of intermetallic compounds and voids at the Cu/solder interfaces. The team built an EM prediction model of interconnects based on a Black-type EM equation, following the JEDEC standard with five test conditions.

After the statistic calculation from the lifetime of samples, the ASE team determined activation energy of Cu pillar interconnects in the FCQFN package (1.12 ± 0.03 eV). The maximum current of the Cu pillar interconnects allowable lasting 10 years at a 105°C operating temperature at a 0.1% failure rate was larger than 2A for the FCQFN Cu pillar structure. “The FCQFN package has great potential in terms of its excellent anti-EM performance for future high-power applications,” the article said.

Designing/manufacturing for EM resiliency
Building electromigration resilience into advanced devices begins with using only EM-compliant linewidths in circuit designs based on the current density and heat profile that the interconnects will experience during operation over the lifetime of the device. Electromigration mitigation also requires process and materials engineering to ensure durability, for instance, of copper pillar bumps under BGA packages. It also calls for an optimized assembly process window and tight process control to prevent tiny violations of design rules that can later precipitate as EM failures.

As the industry makes its way toward true 3D packages, and eventually 3D-ICs, it seems clear that modeling and simulation will play an increasing role in determining many of the guard rails for manufacturing and assembly before manufacturing and assembly even begins. “Reliability modeling and simulation tools are being used to better understand the reliability of bump structures. This proactive approach helps in identifying potential issues before they arise, enabling engineers to implement preventive measures,” said Zwenger.

Modeling and simulation at the system level also will be essential to understanding the complex interplay between reliability mechanisms with thermal and mechanical stress in multi-chiplet systems during operation.

“Electromigration for stacked die is challenging,” said Synopsys’ Lynch. “Localized, die-to-die workloads cause repetitive current flow in specific areas. This generates local heat, increasing EM resulting in wire degradation, while producing even more heat. Reducing the thermal issue becomes critical to ensuring EM reliability.”

As stated previously, solder bumps can become a site for EM reliability failure. “Engineers fine-tune bump design in terms of bump size, pitch, and shape to ensure uniformity and reliability across the entire package. This includes the adoption of innovative Cu bump structures for improved mechanical and electrical properties,” said Amkor’s Zwenger.

In flip-chip BGA and other flip-chip applications, underfill materials — typically thermoset epoxies — are used to reduce the thermal stresses on solder bumps. “Underfill materials play a critical role in providing mechanical support and thermal stability to the bumps,” Zwenger said. “Engineers are investing in the development of advanced underfill formulations with enhanced properties, such as improved adhesion, thermal conductivity, and stress relief.”

Conclusion
Because of its dependence on temperature, electromigration is a failure mechanism to watch and plan for as devices continue to scale and systems integrators continue to cram more and more chiplets of various functions into advanced packages.

“In advanced technologies, the current density is now in close proximity to the maximum density,” said Synopsys’ Lynch. “Anything that causes an increase in temperature poses a threat. Designers of multi-die systems need to understand the impact of temperature and design systems to remove the heat.”

References

1. JiHye Kwon, “Electromigration Performance Of Fine-Line Cu Redistribution Layer (RDL) For HDFO Packaging,” Semiconductor Engineering, Jan. 18, 2024, https://semiengineering.com/electromigration-performance-of-fine-line-cu-redistribution-layer-rdl-for-hdfo-packaging/
2. -Y. Tsai, et al., “An Electromigration Study of Cu Pillar Interconnects in Flip-chip QFN Packaging under Extreme Conditions for High-power Applications,” 2023 IEEE 25th Electronics Packaging Technology Conference (EPTC), Singapore, 2023, pp. 326-332, doi: 10.1109/EPTC59621.2023.10457564.

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Chiplets: 2023 (EBook)
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The post Electromigration Concerns Grow In Advanced Packages appeared first on Semiconductor Engineering.

The semiconductor industry is preparing for the migration from proprietary chiplet-based systems to a more open chiplet ecosystem, in which chiplets fabricated by different companies of various technologies and device nodes can be integrated in a single package with acceptable yield.

To make this work as expected, the chip industry will have to solve a variety of well-documented technical and business issues, and it will have to rein in some of the grander visions of what’s possible — at least initially. The basic challenge is aligning domain-specific performance demands of end systems, which contain a growing number of chiplets, with the assembly and packaging capabilities and methodologies of IDMs, foundries, and OSATs. This includes the creation of assembly development kits (ADKs) that are roughly the equivalent of process development kits (PDKs), which today are codified with manufacturing specifications.

A PDK provides the appropriate level of detail needed to develop planar chips, marrying design tools with fab processes to achieve a predictable outcome. But making this work for an ADK with heterogeneous chiplets is many times more complex. Design and assembly teams need to manage thermal, mechanical, and electrical co-dependencies that cause electrical and mechanical stress, resulting in warpage, reduced yield, and reliability issues under real-world workloads. Layered on top of this the business and legal issues related to packaging of different devices from different manufacturers.

“Chiplets are a growing trend, especially in the HPC and networking segments, with potential to scale to other applications,” said Gabriela Pereira, technology and market analyst for semiconductor packaging at Yole Intelligence. “The industry has understood that high-end advanced packaging technologies are needed to connect them — but that’s much more complex than it seems. Connecting chiplets requires the design of high-bandwidth interconnections at the package level, which can take different forms — e.g., 2D, 2.5D or 3D — while ensuring that the thermal and power requirements are fulfilled.”

Commercial chiplet-based devices generally are domain-specific, and sometimes developed for a specific workload. So despite a big industry push to create a LEGO-like mix-and-match ecosystem for chiplets — which today includes multiple IP and EDA vendors, foundries, memory suppliers, OSATs, substrate suppliers, etc. — making this work as planned will require time and a massive amount of work.

Fig. 1: System assembly requires tighter coupling between chipmakers and OSATs. Source: ASE

In creating heterogeneous integrated designs, it’s essential to have much tighter collaboration between foundries, IDMs, OSATs, and PCB manufacturers. And because each chiplet-based system will be customized, the number of assembly processes will grow substantially. For example, one OSAT noted that among its ~5,000 customers, there are ~1,000 different assembly processes.

That diversity in products and processes makes it difficult to achieve predictable results by choosing chiplets from a large menu of options.

“We’ve already encountered a lot of limitations including not only the silicon, but also integration and the ecosystem,” said Lihong Cao, senior director at ASE Group, at MEPTEC’s Road to Chiplets forum. She stressed that customers continue to push for a low-cost chiplet assembly process, which is creating constructive tension between developing a sophisticated assembly process and the economic realities of different industry sectors. Computing devices for automotive have a higher cost sensitivity than for data centers, for example, but their chips operate in a harsher environment over a longer lifetime.

What’s needed is a defined set of assembly process recipes — basically, a highly limited menu of choices — that are specific to the end application (HPC, automotive, RF telecommunications) in order to lower the cost of chiplet-based systems. OSATs and foundries already are moving in that direction for high-performance computing. For example, at its 2024 Direct Connect event, Intel shared its six different package processes for chiplets. TSMC and Samsung also offer defined sets of chiplet processes. But the success of these assembly processes requires engineering teams to co-optimize the flows, processes, and materials to best match the system requirements.

Fig. 2: Integrated platform development requires tightly coupled architectural analysis that co-optimizes the system design to architecture to assembly process and packaging material selections. Source: Applied Materials

“Previously, when we designed a system we only had to be worried about the system requirements. Once we start segregating into dies and reassembling them, we have to start looking at other things. We have to worry about putting them together while considering signal integrity between dies, reliability, thermals, etc.,” said Itai Leshniak, director of AI systems solutions at Applied Materials, at the MEPTEC forum. “If we take the case of AI-based computer vision, we can break it down layer by layer — on the hardware side, determining which computer vision processors, sensors, filters are needed to break it down into the architecture at layer. Then we begin to go through how to package all these chiplets, and then which materials to use and how to take advantage of those materials.”

Materials and assembly processes
Conceptually, design engineers will use chiplets to design a system. However, the co-design and integration is far more complicated than assembling a set of LEGO blocks, because the chiplets, interposers, and package substrates come from different design houses and manufacturing facilities. The advanced packaging technologies used to connect chiplets vary with an alphabet soup of names — FOWLP, FOPLP, CoWoS, etc., each of which poses additional design and material choices along with certain process limitations.

Fig. 3: There are a multitude of choices in multi-die packaging from the high-level layout to substrates, materials, bonding methods, and cooling materials. Source: Synopsys

Currently engineering teams determine the tradeoffs among the different packaging options to select materials, derive a process recipe, and determine design rules.

Materials are a good starting point. “Materials are very important because they enable new products and packaging technologies,” Tanja Braun, deputy group manager at the Fraunhofer Institute for Reliability and Microintegration IZM. “As you move into more advanced packaging, process is getting much more complex because you are putting more things together. In the end, it’s a combination of equipment, materials, and process development.”

There are three thermal parameters that are critical in package assembly processes — coefficients of thermal expansion (CTE), glass transition temperature (T_g), and thermal conductivity. These factors affect how a material behaves in manufacturing to packaging processes, as well as how it behaves in the field.

“Challenges for our materials include temperature limitations of different die,” said Rama Puligadda, CTO at Brewer Science. “We have to ensure that the temperatures used for bonding materials don’t exceed the thermal limitations of any of the chips that are being integrated into the package. Additionally, there may be some subsequent processes like redistribution layer (RDL) formation or molding. Our materials have to survive those processes. They have to survive the chemicals they come in contact with throughout the packaging process scheme. Mechanical stresses in the package add additional challenges for bonding materials.”

Within a stack of chiplets-on-substrate with an optional interposer, their material attributes affect the thermal-mechanical stresses between neighboring materials, as well. This directly impacts interconnect dimensional control over a large area substrate area.

“If you go work the numbers, you will find that the level of tolerance and control required is frightening,” said Dick Otte, CEO of Promex Industries. “You’re talking about controlling dimensions equivalent to the width of a grass blade over the length of a football field, so that’s roughly 1 in 100,000.”

The goal is uniform heating of the structure in reflow in order to attain the best process results and to avoid cracking. “When you’re taking it through a 250 degrees centigrade temperature change, then you need to heat up slowly so that the top doesn’t get hot before the bottom does,” said Otte.

Multi-physics to comprehend co-optimization
Multi-physics modeling has become the go-to method for co-optimizing packaging design and assembly process development. That affects both permanent and temporary materials, as well the placement of processors, memories, and other components.

“You always looking to what the customer needs electrically, because that’s going to help define the material set. The material set is broadly applicable to a bunch of speed ranges. As long as you don’t step outside of those electrical specifications, theoretically you should be okay,” said Mike Kelly, vice president of advanced package and technology integration at Amkor Technology.

To save many iterations of empirically based development, engineers can use physics-based simulations to understand the impact of a material set’s properties impact on the assembly process, power/thermals, and mechanical vibrations.

Consider that HPC chiplet products can consume ~1,000 watts at peak performance so the power and thermal interactions need to be fully understood.

“We’ve struggled, as everybody has, with this blizzard of complexity in the different techniques. Not only do they vary across different vendors, but they’re also varying over time,” said Marc Swinnen, director of product marketing at Ansys. “Our approach has been to identify the essentials that need to be worked on. We work jointly with customers to develop a simulation flow that actually achieves what is needed now.”

Materials are just one piece of the puzzle. “Then there’s the assembly stresses that need to be modeled to know whether you can correctly assemble this device. The third one is mechanical vibration,” Swinnen said. “Can your device withstand those regular vibrations? Modeling these attributes ties directly into our mechanical analysis tools — acoustic, thermal, vibration, etc. In the end, you’re going to have to do physics simulation. We’re trying to make it accessible to people in many different forms. But the bedrock of our tool offerings is that we have the meshing simulation and analysis. It’s a question of getting the data in the right format in a way that’s practical and usable.”

Evolving assembly design kits
For conventional packages, OSATs provide design rules for each packaging technology. These need to consider electrical, mechanical and thermal design requirements and manufacturing process limitations. In effect this is a multi-dimensional bounding box. Suppliers perform iterations with the customer to create a product specific process recipe.

Rules cover the macro-level attributes. “At a minimum, what you see from design rules is maximum package size, maximum silicon size, and whether silicon can be [mounted] on both sides of the substrate, such that when you follow these constructions the final product will have a lifetime of 1,000 thermal cycles, for example,” said Fraunhofer’s Braun.

In addition, design rules need to describe routing constraints for the interposer and/or redistribution layer, such as RDL line widths and spaces, ball-grid/pillar/pad size and pitches, and the maximum number of interconnections.

Breaking up a monolithic HPC device into multiple dies shifts some of the semiconductor design/process complexity into the packaging space. That makes things much more complicated. Consider that to connect 10 dies requires on order of 100,000 traces within the interposer’s or substrate’s redistribution layer.

To cope with the complexity at the chip level, the IC industry has long relied upon process design kits (PDKs) to capture design rules in an electronic file that can be imported into EDA tools. Their counterparts, assembly design kits (ADKs), are relatively immature.

“We call it Smart Package,” said Amkor’s Kelly. “It’s an ADK that we give to every customer who’s doing their own design. It is a set of macros, and a customization of a database tailored to a customer’s particular design. For chiplets, it is a high-density fan-out package technology. And it’s cognizant of the limitations for metal density and metal spacing, etc. This makes it easier for us to do design rule checks (DRCs).”

But right now, with the level of customization still required, how an ADK is derived and what it entails is in flux. Partnerships between EDA tool vendors, OSATs, and semiconductor device providers are required.

“We come from the IC world where everything is very rigid,” said Kenneth Larsen, director of 3D-IC product management in Synopsys‘ EDA Group. “On the OSAT side, and maybe this is because it’s so custom, design rules seem like a data sheet. Then you build and optimize the products over time or in collaboration with the OSAT. It’s not an electronic exchange. In the IC world, this would be totally unheard of. While it is possible to tweak a few things, you have a qualification process. And it seems like that’s not there yet for packaging.”

Materials and associated assembly recipes ultimately drive what’s possible for a chiplet-substrate stack in terms of pillar pitch, RDL line widths and spaces, bonding processes, and chiplet placement tolerances. But within a handful of ADKs, there are many possible interactions to consider.

The current focus is on co-optimizing the system design with the chiplet assembly process, leading to an assembly process development flow (see figure 4). This flow considers the needs of customization of an assembly process, and it creates the necessary design rules to be used by package designers.

Fig. 4: Chip-package hybrid flow. Source: ASE

“First you need to define your structure using chiplets. Are you using substrate RDL, 2.5D RDL, or a bridge? After that you need to consider your structure’s materials. What kind of material do you choose to fulfill your electrical performance and the mechanical stress requirements,” said Cao. “After that, you do pre-analysis to ensure all the structures and materials you use are workable in terms of electrical, warpage and mechanical stress.”

The design planning flow also includes the evaluation of die-to-die interconnects through the documents for co-design sign-off.

Conclusion
Before chiplet-based designs can be enabled outside the IDM model, the industry needs to complete the ecosystem that bridges the manufacturing and design complexity. This is because the need to co-optimize the system architecture based on materials, process, and integration capabilities is essential. While this would be easier with a set of well-defined products for the chiplet ecosystem to drive forward on, that has not happened yet.

Engineering teams across the design and manufacturing stack will need to collaborate to choose the appropriate materials, architectures, processes, etc., to develop a final chiplet-based product that is designable. As ASE group’s Cao noted, “An integrated design and manufacturing ecosystem is important. It is very critical to have collaboration among IDM, vendors, materials suppliers. Everyone needs to work together to really enable integration for the real applications.”

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Mini-Consortia Forming Around Chiplets
Commercial chiplet marketplaces are still on the distant horizon, but companies are getting an early start with more limited partnerships.

What Can Go Wrong In Heterogeneous Integration
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The post What Works Best For Chiplets appeared first on Semiconductor Engineering.

Atomic layer deposition (ALD) used to be considered too slow to be of practical use in semiconductor manufacturing, but it has emerged as a critical tool for both transistor and interconnect fabrication at the most advanced nodes.

ALD can be speeded up somewhat, but the real shift is the rising value of precise composition and thickness control at the most advanced nodes, which makes the extra time spent on deposition worthwhile.

ALD is a close cousin of chemical vapor deposition, initially introduced in high volume to the semiconductor industry for hafnium oxide (high-k) gate dielectrics. Both CVD and ALD are inherently conformal processes. Deposition occurs on all surfaces exposed to a precursor gas. In ALD, though, the reaction is self-limiting.

The process works like this: First, a precursor gas (A) is introduced into the process chamber, where it adsorbs onto all available substrate sites. No further adsorption occurs once all surface sites are occupied. An inert purge gas, typically nitrogen or argon, flushes out any remaining precursor gas, then a second precursor (B) is introduced. Precursor B reacts with the chemisorbed precursor A to produce the desired film. Once all of the adsorbed molecules are consumed, the reaction stops. After a second purge step, the cycle repeats.

ALD opportunities expand as features shrink
The step-by-step nature of ALD is both its strength and its weakness. Depositing one monolayer at a time gives manufacturers extremely precise thickness control. Using different precursor gases in different ratios can tune the film composition. Unfortunately, the repeated precursor/purge gas cycles take a lot of time. In an interview, CEA-Leti researcher Rémy Gassilloud estimated that in a single wafer process, two minutes per wafer is the maximum cost-effective process time. But two minutes is only enough time to deposit about a 2nm-thick film.

Some process adjustments can improve throughput. Silicon dioxide ALD often uses large furnaces to process many wafers at once. Plasma activation can ionize reagents and accelerate film formation. Still, Gassilloud estimates that 10nm is the maximum practical thickness for ALD films.

As transistors shrink, though, the number of layers in that thickness range is increasing. Transistor structures also are becoming more complex, requiring deposition on vertical surfaces, into deep trenches, and other places not readily accessible by line-of-sight PVD methods. Replacement gates for gate-all-around transistors, for instance, need a process that can fill nanometer-scale cavities.

As noted above, HfO₂ was the first successful application of ALD in semiconductor manufacturing. Its precursors, HfCl₄ and water, are both chemically simple small molecules, whose by-products are volatile and easily removed. Such simple chemistries are the exception, though. ALD of silicon dioxide typically uses aminosilane precursors.⁠[1] Metal nitrides often have complex metal-organic precursor gases. Gassilloud noted that ligands might be added to a precursor molecule to change its vapor pressure or reactivity, or to facilitate adhesion to the substrate. In selective deposition processes, discussed below, ligands might improve selectivity between growth and non-growth surfaces. These larger molecules can be difficult to insinuate into smaller features, and byproducts can be difficult to remove. Complex byproducts can also become a contamination source.

One of the advantages of ALD is its very low process temperature, typically between 200°C and 300°C. It is thermally compatible with both transistor and interconnect processes in CMOS, as well as with deposition on plastic and other novel substrates. Even so, Aditya Kumar and colleagues at GlobalFoundries showed that precise temperature control is important.^⁠[2] TDMAT (tetrakis- dimethylamino titanium) condensation in a TiN deposition process was a significant source of particle defects. To maintain the desired process temperature, both the precursor and purge gas temperatures matter. Introducing cold purge gas into a warm process chamber can cause rapid condensation.

As ALD has become a mainstream process, the industry has found applications for it beyond core device materials, in a variety of sacrificial and spacer layers. For example, double- and quadruple-patterning schemes often use ALD for “pitch-doubling.” By depositing a spacer material on either side of a patterned “mandrel,” then removing the mandrel, the process can cut the original pitch in half without the need for an additional, more costly lithography step.^⁠[3]

Fig. 1: Self-aligned double patterning with ALD spacers. Source: Creative Commons

Depositing a doped oxide on the vertical silicon fins of a finFET device is a less directional and less damaging alternative to ion implantation.[^⁠4]

Selective deposition brings lateral control
These last two examples depend on surface characteristics to mediate deposition. A precursor might adhere more readily to a hard mask than to the underlying material. The vertical face of a silicon fin might offer more (or fewer) adsorption sites than the horizontal face. Selective deposition on more complicated structures may require a pre-deposited growth template, functionalizing substrate regions to encourage or discourage growth. Selective deposition is especially important in interconnect applications. In general, though, a comprehensive review by Rong Chen and colleagues at Huazhong University of Science and Technology explained that selective deposition methods need to replenish the template material as the film grows while needing a mechanism to selectively remove the unwanted material.⁠[5]

For example, tungsten preferentially deposits on silicon relative to SiO₂, but the selectivity diminishes after only a few cycles. Researchers at North Carolina State University successfully re-passivated the oxide by incorporating hydrogen into the tungsten precursor.[⁠6] Similarly, a group at Eindhoven University of Technology found that SiO₂ preferentially deposited on SiO₂ relative to other oxides for only 10 to 15 cycles. A so-called ABC-cycle — adding acetylacetone (“Inhibitor A”) as an inhibitor every 5 to 10 cycles — restored selectivity.⁠[7]

Alternatively, or in addition, atomic layer etching (ALE) might be used to remove unwanted material. ALE operates in the same step-by-step manner as ALD. The first half of a cycle reacts with the existing surface, weakening the bond to the underlying material. Then, a second step — typically ion bombardment — removes the weakened layer. For example, in ALE etching of silicon, chlorine gas reacts with the surface to form various SiCl_x compounds. The chlorination process weakens the inter-silicon bonds between the surface and the bulk, and the chlorinated layer is easily sputtered away. The layer-by-layer nature of ALE depends on preferential removal of the surface material relative to the bulk (SiCl_x vs. Si in this case). The “ALE window” is the combination of energy and temperature at which the surface layer is completely removed without damaging the underlying material.

Somewhat counter-intuitively, Keren Kanarik and colleagues at Lam Research found that higher ion energies actually expanded the ALE window for silicon etching. High ion energies with short exposure times delayed the onset of silicon sputtering relative to conventional RIE.[8]

Adding and subtracting, one atomic layer at a time
For a long time, the semiconductor industry has been looking for alternatives to process schemes that deposit material, pattern it, then etch most of it away. Wouldn’t it be simpler to only deposit the material we will ultimately need? Meanwhile, atomic layer deposition has been filling the spaces under nanosheets and inside cavities. Bulk deposition and etch tools are still with us, and will be for the foreseeable future. In more and more cases, though, those tools provide the frame while ALD and ALE processes fill in the details.

Correction: Corrected attribution of the work on ABC cycles and selective deposition of SiO₂.

References

1. Wenling Li, et al., “Impact of aminosilane and silanol precursor structure on atomic layer deposition process,”Applied Surface Science, Vol 621, 2023,156869, https://doi.org/10.1016/j.apsusc.2023.156869.
2. Kumar, et al., “ALD TiN Surface Defect Reduction for 12nm and Beyond Technologies,” 2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), Saratoga Springs, NY, USA, 2020, pp. 1-4, doi: 10.1109/ASMC49169.2020.9185271.
3. Shohei Yamauchi, et al., “Extendibility of self-aligned type multiple patterning for further scaling”, Proc. SPIE 8682, Advances in Resist Materials and Processing Technology XXX, 86821D (29 March 2013); https://doi.org/10.1117/12.2011953
4. Kalkofen, et al., “Atomic layer deposition of phosphorus oxide films as solid sources for doping of semiconductor structures,” 2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO), Cork, Ireland, 2018, pp. 1-4, doi: 10.1109/NANO.2018.8626235.
5. Rong Chen et al., “Atomic level deposition to extend Moore’s law and beyond,” 2020 Int. J. Extrem. Manuf. 2 022002 DOI 10.1088/2631-7990/ab83e0
6. B Kalanyan, et al., “Using hydrogen to expand the inherent substrate selectivity window during tungsten atomic layer deposition,” 2016 Chem. Mater. 28 117–26 https://doi.org/10.1021/acs.chemmater.5b03319
7. Alfredo Mameli et al., “Area-Selective Atomic Layer Deposition of SiO2 Using Acetylacetone as a Chemoselective Inhibitor in an ABC-Type Cycle” ACS Nano 2017, 11, 9, 9303–9311. https://doi.org/10.1021/acsnano.7b04701
8. Keren J. Kanarik, et al., “Universal scaling relationship for atomic layer etching,” J. Vac. Sci. Technol. A 39, 010401 (2021); doi: 10.1116/6.0000762

The post Enabling Advanced Devices With Atomic Layer Processes 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.

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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.

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In the world of advanced semiconductor fabrication, creating precise device profiles (edge shapes) is an important step in achieving targeted on-chip electrical performance. For example, saddle fin profiles in a DRAM memory device must be precisely fabricated during process development in order to avoid memory performance issues. Saddle fins were introduced in DRAM devices to increase channel length, prevent short channel effects, and increase data retention times. Critical process equipment settings like etch selectivity, or the gas ratio of the etch process, can significantly impact the shape of fabricated saddle fin profiles. These process and profile changes have significant impact on DRAM device performance. It can be challenging to explore all possible saddle fin profile combinations using traditional silicon testing, since wafer-based testing is time-consuming and expensive. To address this issue, virtual fabrication software (SEMulator3D) can be used to test different saddle fin profile shapes without the time and cost of wafer-based development. In this article, we will review an example of using virtual fabrication for DRAM saddle fin profile development. We will also assess DRAM device performance under different saddle fin profile conditions. This methodology can be used to guide process and integration teams in the development of process recipes and specifications for DRAM devices.

The challenge of exploring different profiles

Imagine that you are a DRAM process engineer, and have received nominal process conditions, device specifications and a target saddle fin profile for a new DRAM design. You would like to explore some different process options and saddle fin profiles to improve the performance of your DRAM device. What should you do? This is a common situation for integration and process engineers during the early R&D stages of DRAM process development.

Traditional methods of exploring saddle fin profiles are difficult and sometimes impractical. These methods involve the creation of a series of unique saddle fin profiles on silicon wafers. The process is time-consuming, expensive, and in many cases impractical, due to the large number of scenarios that must be tested.

One solution to these challenges is to use virtual fabrication. SEMulator3D allows us to create and analyze saddle fin profiles within a virtual environment and to subsequently extract and compare device characteristics of these different profiles. The strength of this approach is its ability to accurately simulate the real-world performance of these devices, but to do so faster and less-expensively than using wafer-based testing.

Methodology

Let’s dive into the methodology behind our approach:

Creating saddle fin profiles in a virtual environment

First, we input the design data and process flow (or process steps) for our device in SEMulator3D. The software can then generate a “virtual” 3D DRAM structure and provide a visualization of saddle fin profiles (figure 1). In figure 1(a), a full 3D DRAM structure including the entire simulation domain is displayed. To enable detailed device study, we have cropped a small portion of the simulation domain from this large 3D area. In figure 1(b), we have extracted a cross sectional view of the saddle fin structure, which can be modified by varying a set of multi-etch steps in the process model. The section of the saddle fin that we would like to modify is identified as the “AA” (active area). We can finely tune the etch taper angle, AA/fin CD, fin height, taper angle and additional nominal device parameters to modify the AA profile.

Fig. 1: Process flow set up by SEMulator3D: (a) DRAM structure and (b) Cross section view of saddle fin along with key specifications of the saddle fin profile.

Using the structures that we have built in SEMulator3D, we can next assign dopants and ports to the simulated structure and perform electrical performance evaluation. Accurately assigning dopant species, and defining dopant concentrations within the structure, is critical to ensuring the accuracy of our simulation. In figure 2(a), we display a dopant concentration distribution generated in SEMulator3D.

Ports are contact points in the model which are used to apply or extract electrical signals during a device study. Proper assignment of the ports is very important. Figure 2(b) provides an example of port assignment in our test DRAM structure. By accurately assigning the ports and dopants, we can extract the device’s electrical characteristics under different process scenarios.

Fig. 2: (a) Dopant concentration and (b) Port assignments (in blue).

Manufacturability validation

It is important to ensure that our simulation models match real world results. We can validate our model against cross-sectional images (SEM or TEM images) from an actual fabricated device. To ensure that our simulated device matches the behavior of an actual manufactured chip, we can create real silicon test wafers containing DRAM structures with different saddle fin profiles. To study different saddle fin profiles, we will use different etch recipes on an etch machine to vary the DRAM wordline etch step. This allows us to create specific saddle fin profiles in silicon that can be compared to our simulated profiles. A process engineer can change etch recipes and easily create silicon-based etch profiles that match simulated cross section images, as shown in figure 3. In this case, the engineer created a nominal (Process of Record) profile, a “round” profile (with a rounded top), and a triangular shaped profile (with a triangular top). This wafer-based data is not only used to test electrical performance of the DRAM under different saddle fin profile conditions, but can also be fed back into the virtual model to calibrate the model and ensure that it is accurate during future use.

Fig. 3: Cross section images vs. models: (a) Nominal condition (Process of Record), (b) Round profile and (c) Triangle profile.

Device simulation and validation

In the final stage of our study, we will review the electrical simulation results for different saddle fin profile shapes. Figure 4 displays simulated electrical performance results for the round profile and triangular saddle fin profile. For each of the two profiles, the value of the transistor Subthreshold Swing (SS), On Current (Ion), and Threshold Voltage (Vt) are displayed, with the differences shown. Process integration engineers can use this type of simulation to compare device performance using different process approaches. The same electrical performance differences (trend) were seen on actual fabricated devices, validating the accuracy and reliability of our simulation approach.

Fig. 4: Device electrical simulation results: the transistor performance difference between the Round and Triangular Saddle Fin profile is shown for Subthreshold Swing (SS), On Current (Ion) and Threshold Voltage (Vt).

Conclusions

SEMulator3D provides numerous benefits for the semiconductor manufacturing industry. It allows process integration teams to understand device performance under different process scenarios, and lets them easily explore new processes and architectural opportunities. In this article, we reviewed an example of how virtual fabrication can be used to assess DRAM device performance under different saddle fin profile conditions. Figure 5 displays a summary of the virtual fabrication process, and how we used it to understand, optimize and validate different process scenarios.

Fig. 5: Summary of virtual fabrication process.

Virtual fabrication can be used to guide process and integration teams in the development of process recipes and specifications for any new memory or logic device, and to do so at greater speed and lower cost than silicon-based experimentation.

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As device scaling slows down, a key system functional integration technology is emerging: heterogeneous integration (HI). It leverages advanced packaging technology to achieve higher functional density and lower cost per function. With the continuous development of major semiconductor applications such as AI HPC, edge AI and autonomous electrical vehicles, traditional chips are transforming into smaller, well-partitioned chiplets that require chip-to-chip interconnections to be denser, faster and more reliable. This boosts the demand for heterogeneous integration, elevating demand for innovative advanced packaging technologies.

HI uses advanced packaging to integrate chiplets with heterogeneous designs and process nodes into a single package. This allows enterprises to choose optimum process nodes for specific system demands, such as 3nm for computing chiplets, 7nm for radio frequency chiplets, or to quickly produce super chips with specific functions in a cost-effective manner. HI not only aims for higher interconnection density, but also integrates various functional components, such as logic chips, sensors, memory, and others, which are needed to complete the whole system in one package. Overall energy efficiency and performance is greatly improved, while package size can be significantly reduced.

Advanced packaging solutions for AI HPC

The typical high-density advanced package size for AI cloud computing processors is 55mm x 55mm or more, and contains a 5-2-5 (top 5 layers, middle 2 layers, bottom 5 layers) advanced substrate, or even up to 11-2-11 wiring layers. Chiplets can be interconnected by fan-out technology with silicon bridge or 2.5D with Si Interposer as the integration platform. Through this technique, industry aims to gain more computing power within the same space.

ASE provides high-density packaging solutions, including Flip Chip Ball Grid Array (FCBGA), Fan Out Chip-on-Substrate (FOCoS), FOCoS-Bridge and 2.5D. The chip-to-chip interconnections in FCBGA is accomplished through BGA substrate, and its minimum L/S (line width/line spacing) is only about 10μm/10μm. The very popular and in-demand CoWoS (Chip on Wafer on Substrate) is a 2.5D packaging technology that uses RDL (redistribution layer) on Si interposer to connect chiplets, and its L/S can be significantly reduced to 0.5μm/0.5μm.

In the Si interposer of a 2.5D package, all the chiplets are connected in a side-by-side arrangement, and as the required number of chiplets increases, its area becomes larger and larger, resulting in fewer and fewer Si interposer chips that can be made from each 12-inch wafer (generally less than 50). This indeed significantly increases the manufacturing cost of 2.5D packaging. However, not all applications require 0.5μm/0.5μm L/S, so ASE came up with FOCoS, which uses fan-out technology’s RDL to integrate different chiplets, and its L/S can reach 2μm/2μm. This gives alternative solutions to the market with lower costs. In addition, ASE’s FOCoS-Bridge technology uses silicon bridge to provide high-density routing for interconnecting different chips (such as logic chips and memory) in areas that require high-speed transmission and uses Fan-Out RDL to integrate in other areas. As such, it delivers both 0.5μm/0.5μm and 2μm/2μm flexibility in L/S design, while achieving a significant increase in packaging density and bandwidth.

High performance chip-package-system co-design

To achieve the aforementioned high bandwidth, the chip, package, and entire system must be designed together to achieve holistic design optimization instead of just considering the individual parts. When using electronic design automation (EDA) for design optimization, consideration must be given to overall signal change along the entire transmission path, including Cu pillar, RDL fine line, TSV, μbump, etc. Eye diagrams can then be used to analyze the SerDes link’s electrical performance. When designing differential pairs for high-speed signals, it is necessary to reduce return and insertion loss, especially in the operating frequency band. From chip to package to the entire system, Taiwan’s manufacturing advantage lies in the ability to accomplish the turnkey design process, from beginning to end.

Providing more computing power with less energy

The industry is currently focused on optimizing energy efficiency. One of the key questions being asked is whether the power regulation and decoupling components, which were previously located on the system board, can be moved closer to the package or processor chip. There is even talk of redesigning the on-chip power delivery network (PDN), including supplying power directly from the backside of the chip (Backside PDN).

Power integrity design for power delivery network (PDN)

Optimizing power integrity and minimizing noise can be achieved by strategically positioning the capacitor. Ideally, the capacitor should be placed as close to the chip as possible, but this is dependent on the capacitor’s size and the manufacturing process, both of which can impact cost and performance. Traditional surface-mount technology (SMT) capacitors are relatively large, but chip-level silicon capacitors (Si-Cap) are now available that offer decent capacitance values.

UCIe (Universal Chiplet Interconnect Express) Consortium

Traditionally, there are many standard communication protocols (such as Block-to-Block, Memory Bus, or Interconnection Interface Protocols) at the chip level and the board level for system designers. Industry protocols that specify package-level integration are growing, especially given the need for a universal interface for chiplet integration using 2.5D and FOCoS packaging technologies.

In March 2022, Intel invited upstream and downstream manufacturers in the semiconductor industry chain to form the UCIe Consortium, and a standardized data transmission architecture for chiplet integration was introduced to reduce the cost of advanced packaging design. ASE is proud to be a founding member (Promoter member).

ASE offers a diverse range of advanced packaging types. We have developed packaging design specifications that can be integrated with foundry solutions specifications as well as the system requirements of original equipment manufacturers (OEMs) and cloud service providers to create a comprehensive UCIe package standard. The standard can assist in realizing ubiquitous chiplet heterogeneous integration for HPC applications using various advanced packaging technology architectures, such as 2.5D, 3D, FOCoS, Fan-out, EMIB, CoWoS, etc. Headquartered in Taiwan, ASE is enthusiastically participating in the formulation of international standards and relentlessly providing integrated solutions to the global industry.

Heterogeneous integration has been in development for many years. It can be used to integrate not only homogeneous and heterogeneous chiplets but also other passive and active components including connectors, into a single package. Achieving this requires not only advanced packaging technologies but also design and testing coordination. ASE offers a comprehensive one-stop service solution that includes system design, packaging, and testing to help customers shorten chip design cycles and accelerate product innovation.

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