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

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

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

The crucial role of semiconductor testing

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

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

Zero defects commitment

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

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

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

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

Collaboration is key

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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The role of AI and ML in test keeps growing, providing significant time and money savings that often exceed initial expectations. But it doesn’t work in all cases, sometimes even disrupting well-tested process flows with questionable return on investment.

One of the big attractions of AI is its ability to apply analytics to large data sets that are otherwise limited by human capabilities. In the critical design-to-test realm, AI can address problems such as tool incompatibilities between the design set-up, simulation, and ATE test program, which typically slows debugging and development efforts. Some of the most time-consuming and costly aspects of design-to-test arise from incompatibilities between tools.

“During device bring-up and debug, complex software/hardware interactions can expose the need for domain knowledge from multiple teams or stakeholders, who may not be familiar with each other’s tools,” said Richard Fanning, lead software engineer at Teradyne. “Any time spent doing conversions or debugging differences in these set-ups is time wasted. Our toolset targets this exact problem by allowing all set-ups to use the same set of source files so everyone can be sure they are running the same thing.”

ML/AI can help keep design teams on track, as well. “As we drive down this technology curve, the analytics and the compute infrastructure that we have to bring to bear becomes increasingly more complex and you want to be able to make the right decision with a minimal amount of overkill,” said Ken Butler, senior director of business development in the ACS data analytics platform group at Advantest. “In some cases, we are customizing the test solution on a die-by-die type of basis.”

But despite the hype, not all tools work well in every circumstance. “AI has some great capabilities, but it’s really just a tool,” said Ron Press, senior director of technology enablement at Siemens Digital Industries Software, in a recent presentation at a MEPTEC event. “We still need engineering innovation. So sometimes people write about how AI is going to take away everybody’s job. I don’t see that at all. We have more complex designs and scaling in our designs. We need to get the same work done even faster by using AI as a tool to get us there.”

Speeding design to characterization to first silicon
In the face of ever-shrinking process windows and the lowest allowable defectivity rates, chipmakers continually are improving the design-to-test processes to ensure maximum efficiency during device bring-up and into high volume manufacturing. “Analytics in test operations is not a new thing. This industry has a history of analyzing test data and making product decisions for more than 30 years,” said Advantest’s Butler. “What is different now is that we’re moving to increasingly smaller geometries, advanced packaging technologies and chiplet-based designs. And that’s driving us to change the nature of the type of analytics that we do, both in terms of the software and the hardware infrastructure. But from a production test viewpoint, we’re still kind of in the early days of our journey with AI and test.”

Nonetheless, early adopters are building out the infrastructure needed for in-line compute and AI/ML modeling to support real-time inferencing in test cells. And because no one company has all the expertise needed in-house, partnerships and libraries of applications are being developed with tool-to-tool compatibility in mind.

“Protocol libraries provide out-of-the-box solutions for communicating common protocols. This reduces the development and debug effort for device communication,” said Teradyne’s Fanning. “We have seen situations where a test engineer has been tasked with talking to a new protocol interface, and saved significant time using this feature.”

In fact, data compatibility is a consistent theme, from design all the way through to the latest developments in ATE hardware and software. “Using the same test sequences between characterization and production has become key as the device complexity has increased exponentially,” explained Teradyne’s Fanning. “Partnerships with EDA tool and IP vendors is also key. We have worked extensively with industry leaders to ensure that the libraries and test files they output are formats our system can utilize directly. These tools also have device knowledge that our toolset does not. This is why the remote connect feature is key, because our partners can provide context-specific tools that are powerful during production debug. Being able to use these tools real-time without having to reproduce a setup or use case in a different environment has been a game changer.”

Serial scan test
But if it seems as if all the configuration changes are happening on the test side, it’s important to take stock of substantial changes on the approach to multi-core design for test.

Tradeoffs during the iterative process of design for test (DFT) have become so substantial in the case of multi-core products that a new approach has become necessary.

“If we look at the way a design is typically put together today, you have multiple cores that are going to be produced at different times,” said Siemens’ Press. “You need to have an idea of how many I/O pins you need to get your scan channels, the deep serial memory from the tester that’s going to be feeding through your I/O pins to this core. So I have a bunch of variables I need to trade off. I have the number of pins going to the core, the pattern size, and the complexity of the core. Then I’ll try to figure out what’s the best combination of cores to test together in what is called hierarchical DFT. But as these designs get more complex, with upwards of 2,500 cores, that’s a lot of tradeoffs to figure out.”

Press noted that applying AI with the same architecture can provide a 20% to 30% higher efficiency, but an improved methodology based on packetized scan test (see figure 1) actually makes more sense.

Fig. 1: Advantages to the serial scan network (SSN) approach. Source: Siemens

“Instead of having tester channels feeding into the scan channels that go to each core, you have a packetized bus and packets of data that feed through all the cores. Then you instruct the cores when their packet information is going to be available. By doing this, you don’t have as many variables you need to trade off,” he said. At the core level, each core can be optimized for any number of scan channels and patterns, and the I/O pin count is no longer a variable in the calculation. “Then, when you put it into this final chip, it deliver from the packets the amount of data you need for that core, that can work with any size serial bus, in what is called a serial scan network (SSN).”

Some of the results reported by Siemens EDA customers (see figure 2) highlight both supervised and unsupervised machine learning implementation for improvements in diagnosis resolution and failure analysis. DFT productivity was boosted by 5 to 10X using the serial scan network methodology.

Fig. 2: Realized benefits using machine learning and the serial scan network approach. Source: Siemens

What slows down AI implementation in HVM?
In the transition from design to testing of a device, the application of machine learning algorithms can enable a number of advantages, from better pairing of chiplet performance for use in an advanced package to test time reduction. For example, only a subset of high-performing devices may require burn-in.

“You can identify scratches on wafers, and then bin out the dies surrounding those scratches automatically within wafer sort,” said Michael Schuldenfrei, fellow at NI/Emerson Test & Measurement. “So AI and ML all sounds like a really great idea, and there are many applications where it makes sense to use AI. The big question is, why isn’t it really happening frequently and at-scale? The answer to that goes into the complexity of building and deploying these solutions.”

Schuldenfrei summarized four key steps in ML’s lifecycle, each with its own challenges. In the first phase, the training, engineering teams use data to understand a particular issue and then build a model that can be used to predict an outcome associated with that issue. Once the model is validated and the team wants to deploy it in the production environment, it needs to be integrated with the existing equipment, such as a tester or manufacturing execution system (MES). Models also mature and evolve over time, requiring frequent validation of the data going into the model and checking to see that the model is functioning as expected. Models also must adapt, requiring redeployment, learning, acting, validating and adapting, in a continuous circle.

“That eats up a lot of time for the data scientists who are charged with deploying all these new AI-based solutions in their organizations. Time is also wasted in the beginning when they are trying to access the right data, organizing it, connecting it all together, making sense of it, and extracting features from it that actually make sense,” said Schuldenfrei.

Further difficulties are introduced in a distributed semiconductor manufacturing environment in which many different test houses are situated in various locations around the globe. “By the time you finish implementing the ML solution, your model is stale and your product is probably no longer bleeding edge so it has lost its actionability, when the model needs to make a decision that actually impacts either the binning or the processing of that particular device,” said Schuldenfrei. “So actually deploying ML-based solutions in a production environment with high-volume semiconductor test is very far from trivial.”

He cited a 2014 Google article that stated how the ML code development part of the process is both the smallest and easiest part of the whole exercise, [1] whereas the various aspects of building infrastructure, data collection, feature extraction, data verification, and managing model deployments are the most challenging parts.

Changes from design through test ripple through the ecosystem. “People who work in EDA put lots of effort into design rule checking (DRC), meaning we’re checking that the work we’ve done and the design structure are safe to move forward because we didn’t mess anything up in the process,” said Siemens’ Press. “That’s really important with AI — what we call verifiability. If we have some type of AI running and giving us a result, we have to make sure that result is safe. This really affects the people doing the design, the DFT group and the people in test engineering that have to take these patterns and apply them.”

There are a multitude of ML-based applications for improving test operations. Advantest’s Butler highlighted some of the apps customers are pursuing most often, including search time reduction, shift left testing, test time reduction, and chiplet pairing (see figure 3).

“For minimum voltage, maximum frequency, or trim tests, you tend to set a lower limit and an upper limit for your search, and then you’re going to search across there in order to be able to find your minimum voltage for this particular device,” he said. “Those limits are set based on process split, and they may be fairly wide. But if you have analytics that you can bring to bear, then the AI- or ML-type techniques can basically tell you where this die lies on the process spectrum. Perhaps it was fed forward from an earlier insertion, and perhaps you combine it with what you’re doing at the current insertion. That kind of inference can help you narrow the search limits and speed up that test. A lot of people are very interested in this application, and some folks are doing it in production to reduce search time for test time-intensive tests.”

Fig. 3: Opportunities for real-time and/or post-test improvements to pair or bin devices, improve yield, throughput, reliability or cost using the ACS platform. Source: Advantest

“The idea behind shift left is perhaps I have a very expensive test insertion downstream or a high package cost,” Butler said. “If my yield is not where I want it to be, then I can use analytics at earlier insertions to be able to try to predict which devices are likely to fail at the later insertion by doing analysis at an earlier insertion, and then downgrade or scrap those die in order to optimize downstream test insertions, raising the yield and lowering overall cost. Test time reduction is very simply the addition or removal of test content, skipping tests to reduce cost. Or you might want to add test content for yield improvement,” said Butler.

“If I have a multi-tiered device, and it’s not going to pass bin 1 criteria – but maybe it’s bin 2 if I add some additional content — then people may be looking at analytics to try to make those decisions. Finally, two things go together in my mind, this idea of chiplet designs and smart pairing. So the classic example is a processor die with a stack of high bandwidth memory on top of it. Perhaps I’m interested in high performance in some applications and low power in others. I want to be able to match the content and classify die as they’re coming through the test operation, and then downstream do pick-and-place and put them together in such a way that I maximize the yield for multiple streams of data. Similar kinds of things apply for achieving a low power footprint and carbon footprint.”

Generative AI
The question that inevitably comes up when discussing the role of AI in semiconductors is whether or not large language models like ChatGPT can prove useful to engineers working in fabs. Early work shows some promise.

“For example, you can ask the system to build an outlier detection model for you that looks for parts that are five sigma away from the center line, saying ‘Please create the script for me,’ and the system will create the script. These are the kinds of automated, generative AI-based solutions that we’re already playing with,” says Schuldenfrei. “But from everything I’ve seen so far, there is still quite a lot of work to be done to get these systems to provide outputs with high enough quality. At the moment, the amount of human interaction that is needed afterward to fix problems with the algorithms or models that generative AI is producing is still quite significant.”

A lingering question is how to access the test programs needed to train the new test programs when everyone is protecting important test IP? “Most people value their test IP and don’t necessarily want to set up guardrails around the training and utilization processes,” Butler said. “So finding a way to accelerate the overall process of developing test programs while protecting IP is the challenge. It’s clear this kind of technology is going to be brought to bear, just like we already see in the software development process.”

Failure analysis
Failure analysis is typically a costly and time-consuming endeavor for fabs because it requires a trip back in time to gather wafer processing, assembly, and packaging data specific to a particular failed device, known as a returned material authorization (RMA). Physical failure analysis is performed in an FA lab, using a variety of tools to trace the root cause of the failure.

While scan diagnostic data has been used for decades, a newer approach involves pairing a digital twin with scan diagnostics data to find the root cause of failures.

“Within test, we have a digital twin that does root cause deconvolution based on scan failure diagnosis. So instead of having to look at the physical device and spend time trying to figure out the root cause, since we have scan, we have millions and millions of virtual sample points,” said Siemens’ Press. “We can reverse-engineer what we did to create the patterns and figure out where the mis-compare happened within the scan cells deep within the design. Using YieldInsight and unsupervised machine learning with training on a bunch of data, we can very quickly pinpoint the fail locations. This allows us to run thousands, or tens of thousands fail diagnoses in a short period of time, giving us the opportunity to identify the systematic yield limiters.”

Yet another approach that is gaining steam is using on-die monitors to access specific performance information in lieu of physical FA. “What is needed is deep data from inside the package to monitor performance and reliability continuously, which is what we provide,” said Alex Burlak, vice president of test and analytics at proteanTecs. “For example, if the suspected failure is from the chiplet interconnect, we can help the analysis using deep data coming from on-chip agents instead of taking the device out of context and into the lab (where you may or may not be able to reproduce the problem). Even more, the ability to send back data and not the device can in many cases pinpoint the problem, saving the expensive RMA and failure analysis procedure.”

Conclusion
The enthusiasm around AI and machine learning is being met by robust infrastructure changes in the ATE community to accommodate the need for real-time inferencing of test data and test optimization for higher yield, higher throughput, and chiplet classifications for multi-chiplet packages. For multi-core designs, packetized test, commercialized as an SSN methodology, provides a more flexible approach to optimizing each core for the number of scan chains, patterns and bus width needs of each core in a device.

The number of testing applications that can benefit from AI continues to rise, including test time reduction, V_min/F_max search reduction, shift left, smart pairing of chiplets, and overall power reduction. New developments like identical source files for all setups across design, characterization, and test help speed the critical debug and development stage for new products.

Reference

1. https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

The post AI/ML’s Role In Design And Test Expands appeared first on Semiconductor Engineering.

Okinawa Institute of Science and Technology proposed a new EUV litho technology using only four reflective mirrors and a new method of illumination optics that it claims will use 1/10 the power and cost half as much as existing EUV technology from ASML.

Applied Materials may not receive expected U.S. funding to build a $4 billion research facility in Sunnyvale, CA, due to internal government disagreements over how to fund chip R&D, according to Bloomberg.

SEMI published a position paper this week cautioning the European Union against imposing additional export controls to allow companies, encouraging them to  be “as free as possible in their investment decisions to avoid losing their agility and relevance across global markets.” SEMI’s recommendations on outbound investments are in response to the European Economic Security Strategy and emphasize the need for a transparent and predictable regulatory framework.

The U.S. may restrict China’s access to HBM chips and the equipment needed to make them, reports Bloomberg. Today those chips are manufactured by two Korean-based companies, Samsung and SK hynix, but U.S.-based Micron expects to begin shipping 12-high stacks of HBM3E in 2025, and is currently working on HBM4.

Synopsys executive chair and founder Dr. Aart de Geus was named the winner of the Semiconductor Industry Association’s Robert N. Noyce Award. De Geus was selected due to his contributions to EDA technology over a career spanning more than four decades.

The top three foundries plan to implement high-NA EUV lithography as early as 2025 for the 18 angstrom generation, but the replacement of single exposure high-NA (0.55) over double patterning with standard EUV (NA = 0.33) depends on whether it provides better results at a reasonable cost per wafer.

Quick links to more news:

Global
In-Depth
Market Reports and Earnings
Education and Training
Security
Product News
Research
Events and Further Reading

Global

Belgium-based Imec released part 2 of its chiplets series, addressing testing strategies and standardization efforts, as well as guidelines and research “towards efficient ESD protection strategies for advanced 3D systems-on-chip.”

Also in Belgium, BelGan, maker of GaN chips, filed for bankruptcy according to the Brussels Times.

TSMC‘s Dresden, Germany, plant will break ground this month.

The UK will dole out more than £100 million (~US $128 million) in funding to develop five new quantum research hubs in Glasgow, Edinburgh, Birmingham, Oxford, and London.

MassPhoton is opening Hong Kong‘s first ultra-high vacuum GaN epitaxial wafer pilot line and will establish a GaN research center.

Infineon completed the sale of its manufacturing sites in the Philippines and South Korea to ASE.

Israel-based RAAAM Memory Technologies received a €5.25 million grant from the European Innovation Council (EIC) to support the development and commercialization of its innovative memory solutions. This funding will enable RAAAM to advance its research in high-performance and energy-efficient memory technologies, accelerating their integration into various applications and markets.

In-Depth

Semiconductor Engineering published its Automotive, Security and Pervasive Computing newsletter this week, featuring these top stories and video:

• Chip Security Now Depends On Widening Supply Chain
• Defining Chip Threat Models To Determining Security Risks
• Post-Quantum Computing Threatens Fundamental Transport Protocols

And:

• Where Power Savings Really Count
• Making Electronics More Efficient (video)

Market Reports and Earnings

The semiconductor equipment industry is on a positive trajectory in 2024, with moderate revenue growth observed in Q2 after a subdued Q1, according to a new report from Yole Group. Wafer Fab Equipment revenue is projected to grow by 1.3% year-on-year, despite a 12% drop in Q1. Test equipment lead times are normalizing, improving order conditions. Key areas driving growth include memory and logic capital expenditures and high-bandwidth memory demand.

Worldwide silicon wafer shipments increased by 7% in Q2 2024, according to SEMI‘s latest report. This growth is attributed to robust demand from multiple semiconductor sectors, driven by advancements in AI, 5G, and automotive technologies.

The RF GaN market is projected to grow to US $2 billion by 2029, a 10% CAGR, according to Yole Group.

Counterpoint released their Q2 smartphone top 10 report.

Renesas completed their acquisition of EDA firm Altium, best known for its EDA platform and freeware CircuitMaker package.

It’s earnings season and here are recently released financials in the chip industry:

AMD  Advantest   Amkor   Ansys  Arteris   Arm   ASE   ASM   ASML
Cadence  IBM   Intel   Lam Research   Lattice   Nordson   NXP   Onsemi 
Qualcomm   Rambus  Samsung    SK Hynix   STMicro   Teradyne    TI  
Tower  TSMC    UMC  Western Digital

Industry stock price impacts are here.

Education and Training

Rochester Institute of Technology is leading a new pilot program to prepare community college students in areas such as cleanroom operations, new materials, simulation, and testing processes, with the intent of eventual transfer into RIT’s microelectronic engineering program.

Purdue University inked a deal with three research institutions — University of Piraeus, Technical University of Crete, and King’s College London —to develop joint research programs for semiconductors, AI and other critical technology fields.

The European Chips Skills Academy formed the Educational Leaders Board to help bridge the talent gap in Europe’s microelectronics sector.  The Board includes representatives from universities, vocational training providers, educators and research institutions who collaborate on strategic initiatives to strengthen university networks and build academic expertise through ECSA training programs.

Security

The Cybersecurity and Infrastructure Security Agency (CISA) is encouraging Apple users to review and apply this week’s recent security updates.

Microsoft Azure experienced a nearly 10 hour DDoS attack this week, leading to global service disruption for many customers.  “While the initial trigger event was a Distributed Denial-of-Service (DDoS) attack, which activated our DDoS protection mechanisms, initial investigations suggest that an error in the implementation of our defenses amplified the impact of the attack rather than mitigating it,” stated Microsoft in a release.

NIST published:

• “Recommendations For Increasing U.S. Participation and Leadership in Standards Development,” a report outlining cybersecurity recommendations and mitigation strategies.
• Final guidance documents and software to help improve the “safety, security and trustworthiness of AI systems.”
• Cloud Computing Forensic Reference Architecture guide.

Delta Air Lines plans to seek damages after losing $500 million in lost revenue due to security company CrowdStrike‘s software update debacle.  And shareholders are also angry.

Recent security research:

• Physically Secure Logic Locking With Nanomagnet Logic (UT Dallas)
• WBP: Training-time Backdoor Attacks through HW-based Weight Bit Poisoning (UCF)
• S-Tune: SOT-MTJ Manufacturing Parameters Tuning for Secure Next Generation of Computing ( U. of Arizona, UCF)
• Diffie Hellman Picture Show: Key Exchange Stories from Commercial VoWiFi Deployments (CISPA, SBA Research, U. of Vienna)

Product News

Lam Research introduced a new version of its cryogenic etch technology designed to enhance the manufacturing of 3D NAND for AI applications. This technology allows for the precise etching of high aspect ratio features, crucial for creating 1,000-layer 3D NAND.

Fig.1: 3D NAND etch. Source: Lam Research

Alphawave Semi launched its Universal Chiplet Interconnect Express Die-to-Die IP. The subsystem offers 8 Tbps/mm bandwidth density and supports operation at 24 Gbps for D2D connectivity.

Infineon introduced a new MCU series for industrial and consumer motor controls, as well as power conversion system applications. The company also unveiled its new GoolGaN Drive product family of integrated single switches and half-bridges with integrated drivers.

Rambus released its DDR5 Client Clock Driver for next-gen, high-performance desktops and notebooks. The chips include Gen1 to Gen4 RCDs, power management ICs, Serial Presence Detect Hubs, and temperature sensors for leading-edge servers.

SK hynix introduced its new GDDR7 graphics DRAM. The product has an operating speed of 32Gbps, can process 1.5TB of data per second and has a 50% power efficiency improvement compared to the previous generation.

Intel launched its new Lunar Lake Ultra processors. The long awaited chips will be included in more than 80 laptop designs and has more than 40 NPU tera operations per second as well as over 60 GPU TOPS delivering more than 100 platform TOPS.

Brewer Science achieved recertification as a Certified B Corporation, reaffirming its commitment to sustainable and ethical business practices.

Panasonic adopted Siemens’ Teamcenter X cloud product lifecycle management solution, citing Teamcenter X’s Mendix low-code platform, improved operational efficiency and flexibility for its choice.

Keysight validated its 5G NR FR1 1024-QAM demodulation test cases for the first time. The 5G NR radio access technology supports eMBB and was validated on the 3GPP TS 38.521-4 test specification.

Research

In a 47-page deep-dive report, the Center for Security and Emerging Technology delved into all of the scientific breakthroughs from 1980 to present that brought EUV lithography to commercialization, including lessons learned for the next emerging technologies.

Researchers at the Paul Scherrer Institute developed a high-performance X-ray tomography technique using burst ptychography, achieving a resolution of 4nm. This method allows for non-destructive imaging of integrated circuits, providing detailed views of nanostructures in materials like silicon and metals.

MIT signed a four-year agreement with the Novo Nordisk Foundation Quantum Computing Programme at University of Copenhagen, focused on accelerating quantum computing hardware research.

MIT’s Research Laboratory of Electronics (RLE) developed a mechanically flexible wafer-scale integrated photonics fabrication platform. This enables the creation of flexible photonic circuits that maintain high performance while being bendable and stretchable. It offers significant potential for integrating photonic circuits into various flexible substrate applications in wearable technology, medical devices, and flexible electronics.

The Naval Research Lab identified a new class of semiconductor nanocrystals with bright ground-state excitons, emphasizing an important advancement in optoelectronics.

Researchers from National University of Singapore developed a novel method, known as tension-driven CHARM3D,  to fabricate 3D self-healing circuits, enabling the 3D printing of free-standing metallic structures without the need for support materials and external pressure.

Find more research in our Technical Papers library.

Events and Further Reading

Find upcoming chip industry events here, including:

Upcoming webinars are here, including topics such as quantum safe cryptography, analytics for high-volume manufacturing, and mastering EMC simulations for electronic design.

Find Semiconductor Engineering’s latest newsletters here:

Automotive, Security and Pervasive Computing
Systems and Design
Low Power-High Performance
Test, Measurement and Analytics
Manufacturing, Packaging and Materials

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

Okinawa Institute of Science and Technology proposed a new EUV litho technology using only four reflective mirrors and a new method of illumination optics that it claims will use 1/10 the power and cost half as much as existing EUV technology from ASML.

Applied Materials may not receive expected U.S. funding to build a $4 billion research facility in Sunnyvale, CA, due to internal government disagreements over how to fund chip R&D, according to Bloomberg.

SEMI published a position paper this week cautioning the European Union against imposing additional export controls to allow companies, encouraging them to  be “as free as possible in their investment decisions to avoid losing their agility and relevance across global markets.” SEMI’s recommendations on outbound investments are in response to the European Economic Security Strategy and emphasize the need for a transparent and predictable regulatory framework.

The U.S. may restrict China’s access to HBM chips and the equipment needed to make them, reports Bloomberg. Today those chips are manufactured by two Korean-based companies, Samsung and SK hynix, but U.S.-based Micron expects to begin shipping 12-high stacks of HBM3E in 2025, and is currently working on HBM4.

Synopsys executive chair and founder Dr. Aart de Geus was named the winner of the Semiconductor Industry Association’s Robert N. Noyce Award. De Geus was selected due to his contributions to EDA technology over a career spanning more than four decades.

The top three foundries plan to implement high-NA EUV lithography as early as 2025 for the 18 angstrom generation, but the replacement of single exposure high-NA (0.55) over double patterning with standard EUV (NA = 0.33) depends on whether it provides better results at a reasonable cost per wafer.

Quick links to more news:

Global
In-Depth
Market Reports and Earnings
Education and Training
Security
Product News
Research
Events and Further Reading

Global

Belgium-based Imec released part 2 of its chiplets series, addressing testing strategies and standardization efforts, as well as guidelines and research “towards efficient ESD protection strategies for advanced 3D systems-on-chip.”

Also in Belgium, BelGan, maker of GaN chips, filed for bankruptcy according to the Brussels Times.

TSMC‘s Dresden, Germany, plant will break ground this month.

The UK will dole out more than £100 million (~US $128 million) in funding to develop five new quantum research hubs in Glasgow, Edinburgh, Birmingham, Oxford, and London.

MassPhoton is opening Hong Kong‘s first ultra-high vacuum GaN epitaxial wafer pilot line and will establish a GaN research center.

Infineon completed the sale of its manufacturing sites in the Philippines and South Korea to ASE.

Israel-based RAAAM Memory Technologies received a €5.25 million grant from the European Innovation Council (EIC) to support the development and commercialization of its innovative memory solutions. This funding will enable RAAAM to advance its research in high-performance and energy-efficient memory technologies, accelerating their integration into various applications and markets.

In-Depth

Semiconductor Engineering published its Automotive, Security and Pervasive Computing newsletter this week, featuring these top stories and video:

• Chip Security Now Depends On Widening Supply Chain
• Defining Chip Threat Models To Determining Security Risks
• Post-Quantum Computing Threatens Fundamental Transport Protocols

And:

• Where Power Savings Really Count
• Making Electronics More Efficient (video)

Market Reports and Earnings

The semiconductor equipment industry is on a positive trajectory in 2024, with moderate revenue growth observed in Q2 after a subdued Q1, according to a new report from Yole Group. Wafer Fab Equipment revenue is projected to grow by 1.3% year-on-year, despite a 12% drop in Q1. Test equipment lead times are normalizing, improving order conditions. Key areas driving growth include memory and logic capital expenditures and high-bandwidth memory demand.

Worldwide silicon wafer shipments increased by 7% in Q2 2024, according to SEMI‘s latest report. This growth is attributed to robust demand from multiple semiconductor sectors, driven by advancements in AI, 5G, and automotive technologies.

The RF GaN market is projected to grow to US $2 billion by 2029, a 10% CAGR, according to Yole Group.

Counterpoint released their Q2 smartphone top 10 report.

Renesas completed their acquisition of EDA firm Altium, best known for its EDA platform and freeware CircuitMaker package.

It’s earnings season and here are recently released financials in the chip industry:

AMD  Advantest   Amkor   Ansys  Arteris   Arm   ASE   ASM   ASML
Cadence  IBM   Intel   Lam Research   Lattice   Nordson   NXP   Onsemi 
Qualcomm   Rambus  Samsung    SK Hynix   STMicro   Teradyne    TI  
Tower  TSMC    UMC  Western Digital

Industry stock price impacts are here.

Education and Training

Rochester Institute of Technology is leading a new pilot program to prepare community college students in areas such as cleanroom operations, new materials, simulation, and testing processes, with the intent of eventual transfer into RIT’s microelectronic engineering program.

Purdue University inked a deal with three research institutions — University of Piraeus, Technical University of Crete, and King’s College London —to develop joint research programs for semiconductors, AI and other critical technology fields.

The European Chips Skills Academy formed the Educational Leaders Board to help bridge the talent gap in Europe’s microelectronics sector.  The Board includes representatives from universities, vocational training providers, educators and research institutions who collaborate on strategic initiatives to strengthen university networks and build academic expertise through ECSA training programs.

Security

The Cybersecurity and Infrastructure Security Agency (CISA) is encouraging Apple users to review and apply this week’s recent security updates.

Microsoft Azure experienced a nearly 10 hour DDoS attack this week, leading to global service disruption for many customers.  “While the initial trigger event was a Distributed Denial-of-Service (DDoS) attack, which activated our DDoS protection mechanisms, initial investigations suggest that an error in the implementation of our defenses amplified the impact of the attack rather than mitigating it,” stated Microsoft in a release.

NIST published:

• “Recommendations For Increasing U.S. Participation and Leadership in Standards Development,” a report outlining cybersecurity recommendations and mitigation strategies.
• Final guidance documents and software to help improve the “safety, security and trustworthiness of AI systems.”
• Cloud Computing Forensic Reference Architecture guide.

Delta Air Lines plans to seek damages after losing $500 million in lost revenue due to security company CrowdStrike‘s software update debacle.  And shareholders are also angry.

Recent security research:

• Physically Secure Logic Locking With Nanomagnet Logic (UT Dallas)
• WBP: Training-time Backdoor Attacks through HW-based Weight Bit Poisoning (UCF)
• S-Tune: SOT-MTJ Manufacturing Parameters Tuning for Secure Next Generation of Computing ( U. of Arizona, UCF)
• Diffie Hellman Picture Show: Key Exchange Stories from Commercial VoWiFi Deployments (CISPA, SBA Research, U. of Vienna)

Product News

Lam Research introduced a new version of its cryogenic etch technology designed to enhance the manufacturing of 3D NAND for AI applications. This technology allows for the precise etching of high aspect ratio features, crucial for creating 1,000-layer 3D NAND.

Fig.1: 3D NAND etch. Source: Lam Research

Alphawave Semi launched its Universal Chiplet Interconnect Express Die-toDie IP. The subsystem offers 8 Tbps/mm bandwidth density and supports operation at 24 Gbps for D2D connectivity.

Infineon introduced a new MCU series for industrial and consumer motor controls, as well as power conversion system applications. The company also unveiled its new GoolGaN Drive product family of integrated single switches and half-bridges with integrated drivers.

Rambus released its DDR5 Client Clock Driver for next-gen, high-performance desktops and notebooks. The chips include Gen1 to Gen4 RCDs, power management ICs, Serial Presence Detect Hubs, and temperature sensors for leading-edge servers.

SK hynix introduced its new GDDR7 graphics DRAM. The product has an operating speed of 32Gbps, can process 1.5TB of data per second and has a 50% power efficiency improvement compared to the previous generation.

Intel launched its new Lunar Lake Ultra processors. The long awaited chips will be included in more than 80 laptop designs and has more than 40 NPU tera operations per second as well as over 60 GPU TOPS delivering more than 100 platform TOPS.

Brewer Science achieved recertification as a Certified B Corporation, reaffirming its commitment to sustainable and ethical business practices.

Panasonic adopted Siemens’ Teamcenter X cloud product lifecycle management solution, citing Teamcenter X’s Mendix low-code platform, improved operational efficiency and flexibility for its choice.

Keysight validated its 5G NR FR1 1024-QAM demodulation test cases for the first time. The 5G NR radio access technology supports eMBB and was validated on the 3GPP TS 38.521-4 test specification.

Research

In a 47-page deep-dive report, the Center for Security and Emerging Technology delved into all of the scientific breakthroughs from 1980 to present that brought EUV lithography to commercialization, including lessons learned for the next emerging technologies.

Researchers at the Paul Scherrer Institute developed a high-performance X-ray tomography technique using burst ptychography, achieving a resolution of 4nm. This method allows for non-destructive imaging of integrated circuits, providing detailed views of nanostructures in materials like silicon and metals.

MIT signed a four-year agreement with the Novo Nordisk Foundation Quantum Computing Programme at University of Copenhagen, focused on accelerating quantum computing hardware research.

MIT’s Research Laboratory of Electronics (RLE) developed a mechanically flexible wafer-scale integrated photonics fabrication platform. This enables the creation of flexible photonic circuits that maintain high performance while being bendable and stretchable. It offers significant potential for integrating photonic circuits into various flexible substrate applications in wearable technology, medical devices, and flexible electronics.

The Naval Research Lab identified a new class of semiconductor nanocrystals with bright ground-state excitons, emphasizing an important advancement in optoelectronics.

Researchers from National University of Singapore developed a novel method, known as tension-driven CHARM3D,  to fabricate 3D self-healing circuits, enabling the 3D printing of free-standing metallic structures without the need for support materials and external pressure.

Find more research in our Technical Papers library.

Events and Further Reading

Find upcoming chip industry events here, including:

Upcoming webinars are here, including topics such as quantum safe cryptography, analytics for high-volume manufacturing, and mastering EMC simulations for electronic design.

Find Semiconductor Engineering’s latest newsletters here:

Automotive, Security and Pervasive Computing
Systems and Design
Low Power-High Performance
Test, Measurement and Analytics
Manufacturing, Packaging and Materials

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

One of the big challenges for IC test is making sense of mountains of data, a direct result of more features being packed onto a single die, or multiple chiplets being assembled into an advanced package. Collecting all that data through various agents and building models on the tester no longer makes sense for a couple reasons — there is too much data, and there are multiple customers using the same equipment. Steve Zamek, director of product management at PDF Solutions, and Eli Roth, product manager at Teradyne, explain how to optimize testing around different data sources, how to partition that data between the edge and the cloud, and how to ensure it remains secure.

The post Making Adaptive Test Work Better appeared first on Semiconductor Engineering.