Semiconductor manufacturing creates a wealth of data – from materials, products, factory subsystems and equipment. But how do we best utilize that information to optimize processes and reach the goal of zero defect manufacturing?

This is a topic we first explored in our previous blog, “Achieving Zero Defect Manufacturing Part 1: Detect & Classify.” In it, we examined real-time defect classification at the defect, die and wafer level. In this blog, the second in our three-part series, we will discuss how to use root cause analysis to determine the source of defects. For starters, we will address the software tools needed to properly conduct root cause analysis for a faster understanding of visual, non-visual and latent defect sources.

About software

The software platform fabs choose impacts how well users are able to integrate data, conduct database analytics and perform server-side and real-time analytics. Manufacturers want the ability to choose a platform that can scale by data volume, type and multisite integration. In addition, all of this data – whether it is coming from metrology, inspection or testing – must be normalized before fabs can apply predictive modeling and machine learning based analytics to find the root cause of defects and failures. This search, however, goes beyond a simple examination of process steps and tools; manufacturers also need a clear understanding of each device’s genealogy. In addition, fabs should employ an AI-based yield optimizer capable of running multiple models and offering potential optimization measures that can be taken in the factory to improve the process.

Now that we have discussed software needs, we will turn our attention to two use cases to further our examination of root cause analysis in zero defect manufacturing.

Root Cause Case No. 1

The first root cause value case we would like to discuss involves the integration of wafer probe, photoluminescence and epitaxial (epi) data. Previously, integrating these three kinds of data was not possible because the identification for wafers and lots – pre- and post-epi – were generally not linked. Wafers and lots were often identified by entirely different names before and after the epi step. For reasons that do not need to be explained, this was a huge hindrance to advancing the goal of zero defect manufacturing because the impact of the epi process on yield was not detected in a timely manner, resulting in higher defectivity and yield loss.

But the challenge is not as simple as identification and naming practices. Typical wafer ID trackers are not applied prior to the post-epi step because of technical and logistical constraints. The solution is for fabs to employ defect and yield analytics software that will enable genealogy that can link data from the epi and pre-epi processes to post-epi processes. The real innovation occurs when the genealogical information is normalized and interpolated with electrical test data. Once integrated, this data offers users a more complete understanding of where yield limiting events are occurring.

Fig. 1: Photoluminescence map (left) and electrical test performance by epi tool (right).

For example, let us consider the following scenario: in figure 1 (left) we show a group of dies that negatively affect performance on the upper left edge of the wafer. Through more traditional measures, this pocket of defectivity may have gone unnoticed, allowing for bad die to move forward in the process. But by applying integrated data, genealogical information and electrical test data, this trouble-plagued area was identified down to the epi tool and chamber (figure 1, right), and the defective material was prevented from going forward in the process. As significant as this is, with the right software platform this approach enables root cause analysis to be conducted in minutes, not days.

Now, onto the second use case in which we look at how to problem solve within the supply chain.

Root Cause Case No. 2

During final test and measurement, chips sometimes fail. In many cases, the faulty chips were previously determined to be good chips and were advanced forward in the process as a result of combining multiple chips coming from different products, lots, or wafers. The important thing here is to understand why this happens.

When there is a genealogy model in a yield software platform, fabs are able to pick the lots and wafers where bad chips come from and then run this information through pattern analysis software. In one particular scenario (figure 2), users were able to apply pattern analysis software to discover that all of the defective die arose from a spin coater issue, in this case, a leak negatively impacting the underbump metallization area following typical preventive maintenance measures.

To compensate for this, the team used integrated analytics to create a fault detection and classification (FDC) model to identify similar circumstances going forward. In this case, the FDC model monitors the suction power of the spin coater. If suction power for more than 10 consecutive samples are above the set limit, alarms are triggered and an appropriate Out of Control Action Plan (OCAP) process is executed that includes notification to tool owner.

Fig. 2: Proactive zero defect manufacturing at-a-glance.

The above explains how fabs are able to turn reactive root cause analytics into proactive monitoring. With such an approach, manufacturers can monitor for this and other issues and avoid the advancement of future defective die. Furthermore, the number of defect signatures that can be monitored inline can be as high as 40 different signatures, if not more. And in case these defects are missed at the process level, they can be identified at the inspection level or post-inspection, avoiding hundreds of issues further along in the process.

Conclusion

Zero defect manufacturing is not so much of a goal as it is a commitment to root out defects before they happen. To accomplish this, fabs need a wealth of data from the entire process to achieve a clear picture of what is going wrong, where it is going wrong and why it is going wrong. In this blog, we offered specific scenarios where root cause analysis was used to find defects across wafers and dies. However, these are just a few examples of how software can be used to find difficult-to-find defects. It can be beneficial in many different areas across the entire process, with each application further strengthening a fab’s efforts to employ a zero defect manufacturing approach, increasing yield and meeting the stringent requirements of some of the industry’s most advanced customers.

In our next blog, we will discuss how to detect dormant defects, use feedback and feedforward measures, and monitor the health of process control equipment. We hope you join us as we continue to explore methods for achieving zero defect manufacturing.

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In the age where data reigns supreme, the semiconductor industry stands on the cusp of revolutionary change, redefining complexity and productivity through a lens crafted by artificial intelligence (AI). The intersection of AI and the semiconductor industry is not merely an emerging trend—it is the fulcrum upon which the next generation of technological innovation balances. Semiconductor companies are facing a critical juncture where the burgeoning complexity of chip designs is outpacing the growth of skilled human resources. This is where the infusion of AI-powered data analytics catalyzes a seismic shift in the industry’s approach to efficiency and productivity.

AI in semiconductor design: A revolution beckons

With technological leaps like 5G, AI, and autonomous vehicles driving chip demand, the status quo for semiconductor design is no longer sustainable. Traditional design methodologies fall short in addressing the challenges presented by these new technologies, and the need for a new approach is non-negotiable. AI, with its capacity to process massive datasets and learn from patterns, offers a revolutionary solution. Gathered with vast amounts of electronic design automation (EDA) data, machine learning algorithms can pave an efficient path through design complexities.

Navigating the complexity of EDA data with AI

The core of semiconductor design lies in the complexity of EDA data, which is often disparate, unstructured, and immensely intricate, existing in various formats ranging from simple text to sophisticated binary machine-readable data. AI presents a beacon of hope in taming this beast by enabling the industry to store, process, and analyze data with unprecedented efficiency.

AI-enabled data analytics offer a path through the labyrinth of EDA complexity, providing a scalable and sophisticated data storage and processing solution. By harnessing AI’s capabilities, the semiconductor industry can dissect, organize, and distill data into actionable insights, elevating the efficacy of chip design processes.

Leveraging AI in design excellence

Informed decisions are the cornerstone of successful chip design, and the fusion of AI-driven analytics with semiconductor engineering marks a watershed moment in the industry. AI’s ability to comprehend and process unstructured data at scale enables a deeper understanding of design challenges, yielding solutions that optimize SoCs’ power, performance, and area (PPA).

AI models, fed by fragmented data points from EDA compilation, can predict bottlenecks, performance constraints, or power inefficiencies before they impede the design process. This foresight empowers engineers with informed design decisions, fostering an efficient and anticipatory design culture.

Reimagining engineering team efficiency

One of the most significant roadblocks in the semiconductor industry has been aligning designer resources with the exponential growth of chip demand. As designs become complex, they evolve into multifaceted systems on chips (SoCs) housing myriad hierarchical blocks that accumulate vast amounts of data throughout the iterative development cycle. When harnessed effectively, this data possesses untapped potential to elevate the efficiency of engineering teams.

Consolidating data review into a systematic, knowledge-driven process paves the way for accelerated design closure and seamless knowledge transfer between projects. This refined approach can significantly enhance the productivity of engineering teams, a crucial factor if the semiconductor industry is to meet the burgeoning chip demand without exponentially expanding design teams.

Ensuring a systemic AI integration

For the full potential of AI to be realized, a systemic integration across the semiconductor ecosystem is paramount. This integration spans the collection and storage of data and the development of AI models attuned to the industry’s specific needs. Robust AI infrastructure, equipped to handle the diverse data formats, is the cornerstone of this integration. AI models must complement it and be fine-tuned to the peculiarities of semiconductor design, ensuring that the insights they produce are accurate and actionable.

Cultivating AI competencies within engineering teams

As AI plays a central role in the semiconductor industry, it highlights the need for AI competencies within engineering teams. Upskilling the workforce to leverage AI tools and platforms is a critical step toward a harmonized AI ecosystem. This journey toward proficiency entails familiarization with AI concepts and a collaborative approach that blends domain expertise with AI acumen. Engineering teams adept at harnessing AI can unlock its full potential and become pioneers of innovation in the semiconductor landscape.

Intelligent system design

At Cadence, the conception of technological ecosystems is encapsulated within a framework of three concentric circles—a model neatly epitomized by the sophistication of an electric vehicle. The first circle represents the data used by the car; the second circle represents the physical car, including the mechanical, electrical, hardware, and software components. The third circle represents the silicon that powers the entire system.

The Cadence.AI Platform operates at the vanguard of pervasive intelligence, harnessing data and AI-driven analytics to propel system and silicon design to unprecedented levels of excellence. By deploying Cadence.AI, we converge our computational software innovations, from Cadence’s Verisium AI-Driven Verification Platform to the Cadence Cerebrus Intelligent Chip Explorer’s AI-driven implementation.

The AI-driven future of semiconductor innovation

The implications are far-reaching as the semiconductor industry charts its course into an AI-driven era. AI promises to redefine design efficiency, expedite time to market, and pioneer new frontiers in chip innovation. The path forward demands a concerted effort to integrate AI seamlessly into the semiconductor fabric, cultivating an ecosystem primed for the challenges and opportunities ahead.

Semiconductor firms that champion AI adoption will set the standard for the industry’s evolution, carving a niche for themselves as pioneers of a new chip design and production paradigm. The future of semiconductor innovation is undoubtedly AI, and the time to embrace this transformative force is now.

Cadence is already at the forefront of this AI-led revolution. Our Cadence.AI Platform is a testament to AI’s power in redefining systems and silicon design. By enabling the concurrent creation of multiple designs, optimizing team productivity, and pioneering leaner design approaches, Cadence.AI illustrates the true potential of AI in semiconductor innovation.

The harmonized suite of our AI tools equips our customers with the ability to employ AI-driven optimization and debugging, facilitating the concurrent creation of multiple designs while optimizing the productivity of engineering teams. It empowers a leaner workforce to achieve more, elevating their capability to generate a spectrum of designs in parallel with unmatched efficiency and precision, resulting in a new frontier in design excellence, where AI acts as a co-pilot to the engineering team, steering the way to unparalleled chip performance. Learn more about the power of AI to forge intelligent designs.

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Data management is becoming a significant new challenge for the chip industry, as well as a brand new opportunity, as the amount of data collected at every step of design through manufacturing continues to grow.

Exacerbating the problem is the rising complexity of designs, many of which are highly customized and domain-specific at the leading edge, as well as increasing demands for reliability and traceability. There also is a growing focus on chiplets developed using different processes, including some from different foundries, and new materials such as glass substrates and ruthenium interconnects. On the design side, EDA and verification tools can generate terabytes of data on a weekly or even a daily basis, unlike in the past when this was largely done on a per-project basis.

While more data can be used to provide insights into processes and enable better designs, it’s an ongoing challenge to manage the current volumes being generated. The entire industry must rethink some well-proven methodologies and processes, as well as invest in a variety of new tools and approaches. At the same time, these changes are generating concern in an industry used to proceeding cautiously, one step at a time, based on silicon- and field-proven strategies. Increasingly, AI/ML is being added into design tools to identify anomalies and patterns in large data sets, and many of those tools are being regularly updated as algorithms are updated and new features are added, making it difficult to know exactly when and where to invest, which data to focus on, and with whom to share it.

“Every company has its own design flow, and almost every company has its own methodology around harvesting that data, or best practices about what reports should or should not be written out at what point,” said Rob Knoth, product management director in Cadence’s Digital & Signoff group. “There’s a death by 1,000 cuts that can happen in terms of just generating titanic volumes of data because, in general, disk space is cheap. People don’t think about it a lot, and they’ll just keep generating reports. The problem is that just because you’re generating reports doesn’t mean you’re using them.”

Fig. 1: Rising design complexity is driving increased need for data management. Source: IEEE Rising Stars 2022/Cadence

As with any problem in chip design, there is opportunity in figuring out a path forward. “You can always just not use the data, and then you’re back where you started,” said Tony Chan Carusone, CTO at Alphawave Semi. “The reason it becomes a problem for organizations is because they haven’t architected things from the beginning to be scalable, and therefore, to be able to handle all this data. Now, there’s an opportunity to leverage data, and it’s a different way. So it’s disruptive because you have to tear things apart, from re-architecting systems and processes to how you collect and store data, and organize it in order to take advantage of the opportunity.”

Buckets of data, buckets of problems
The challenges that come with this influx of data can be divided into three buckets, said Jim Schultz, senior staff product manager at Synopsys. The first is figuring out what information is actually critical to keep. “If you make a run, designers tend to save that run because if they need to do a follow up run, they have some data there and they may go, ‘Okay, well, what’s the runtime? How long did that run take, because my manager is going to ask me what I think the runtime is going to be on the next project or the next iteration of the block. While that data may not be necessary, designers and engineers have a tendency to hang onto it anyway, just in case.”

The second challenge is that once the data starts to pour in, it doesn’t stop, raising questions about how to manage collection. And third, once the data is collected, how can it be put to best use?

“Data analytics have been around with other types of companies exploring different types of data analytics, but the differences are those are can be very generic solutions,” said Schultz. “What we need for our industry is going to be very specific data analytics. If I have a timing issue, I want you to help me pinpoint what the cause of that timing violation is. That’s very specific to what we do in EDA. When we talk about who is cutting through the noise, we don’t want data that’s just presented. We want the data that is what the designer most cares about.”

Data security
The sheer number of tools being used and companies and people involved along the design pathway raises another challenge — security.

“There’s a lot of thought and investment going into the security aspect of data, and just as much as the problem of what data to save and store is the type of security we have to have without hindering the user day-to-day,” said Simon Rance, director of product management at Keysight. “That’s becoming a bigger challenge. Things like the CHIPS Act and the geopolitical scenarios we have at the moment are compounding that problem because a lot of the companies that used to create all these devices by themselves are having to collaborate, even with companies in different regions of the globe.”

This requires a balancing act. “It’s almost like a recording studio where you have all these knobs and dials to fine tune it, to make sure we have security of the data,” said Rance. “But we’re also able to get the job done as smoothly and as easily as we can.”

Further complicating the security aspect is that designing chips is not a one-man job. As leading-edge chips become increasingly complex and heterogeneous, they can involve hundreds of people in multiple companies.

“An important thing to consider when you’re talking about big data and analytics is what you’re going to share and with whom you’re going to share it,” said Synopsys’ Schultz. “In particular, when you start bringing in and linking data from different sources, if you start bringing in data related to silicon performance, you don’t want everybody to have access to that data. So the whole security protocol is important.”

Even the mundane matters — having a ton of data makes it likely, at some point, that data will be moved.

“The more places the data has to be transferred to, the more delays,” said Rance. “The bigger the data set, the longer it takes to go from A to B. For example, a design team in the U.S. may be designing during the day. Then, another team in Singapore or Japan will pick up on that design in their time zone, but they’re across the world. So you’re going to have to sync the data back and forth between these kinds of design sites. The bigger the data, the harder to sync.”

Solutions
The first step toward solving the issue of too much data is figuring out what data is actually needed. Rance said his team has found success using smart algorithms that help figure out which data is essential, which in turn can help optimize storage and transfer times.

There are less technical problems that can rear their heads, as well. Gina Jacobs, head of global communications and brand marketing at Arteris, said that engineers who use a set methodology — particularly those who are used to working on a problem by themselves and “brute forcing” a solution – also can find themselves overwhelmed by data.

“Engineers and designers can also switch jobs, taking with them institutional knowledge,” Jacobs said. “But all three problems can be solved with a single solution — having data stored in a standardized way that is easily accessible and sortable. It’s about taking data and requirements and specifications in different forms and then having it in the one place so that the different teams have access to it, and then being able to make changes so there is a single source of truth.”

Here, EDA design and data management tools are increasingly relying on artificial intelligence to help. Schultz forecasted a future where generative AI will touch every facet of chip development. “Along with that is the advanced data analytics that is able to mine all of that data you’ve been collecting, instead of going beyond the simple things that people have been doing, like predicting how long runtime is going to be or getting an idea what the performance is going to be,” he said. “Tools are going to be able to deal with all of that data and recognize trends much faster.”

Still, those all-encompassing AI tools, capable of complex analysis, are still years away. Cadence’s Knoth said he’s already encountered clients that are reluctant to bring it into the mix due to fears over the costs involved in disk space, compute resources, and licenses. Others, however, have been a bit more open-minded.

“Initially, AI can use a lot of processors to generate a lot of data because it’s doing a lot of things in parallel when it’s doing the inferencing, but it usually gets to the result faster and more predictably,” he said. So while a machine learning algorithm may generate even more vast amounts of data, on top of the piles currently available, “a good machine learning algorithm could be watching and smartly killing or restarting jobs where needed.”

As for the humans who are still an essential component to chip design, Alphawave’s Carusone said hardware engineers should take a page from lessons learned years ago from their counterparts in the software development world.

These include:

• Having an organized and automated way to collect data, file it in a repository, and not do anything manually;
• Developing ways to run verification and lab testing and everything in between in parallel, but with the data organized in a way that can be mined; and
• Creating methods for rigorously checking in and out of different test cases that you want to consider.

“The big thing is you’ve got all this data collected, but then what is each of each of those files, each of those collections of data?” said Carusone. “What does that correspond to? What test conditions was that collected in? The software community dealt with that a while ago, and the hardware community also needs to have this under its belt, taking it to the next level and recognizing we really need to be able to do this en masse. We need to be able to have dozens of people work in parallel, collecting data and have it all on there. We can test a big collection of our designs in the lab without anyone having to touch a thing, and then also try refinements of the firmware, scale them out, then have all the data come in and be analyzed. Being able to have all that done in an automated way lets you track down and fix problems a lot more quickly.”

Conclusion
The influx of new tools used to analyze and test chip designs has increased productivity, but those designs come with additional considerations. Institutions and individual engineers and designers have never had access to so much data, but that data is of limited value if it’s not used effectively.

Strategies to properly store and order that data are essential. Some powerful tools are already in place to help do that, and the AI revolution promises to make even more powerful resources available to quickly cut down on the time needed to run tests and analyze the results.

For now, handling all that data remains a tricky balance, according to Cadence’s Knoth. “If this was an easy problem, it wouldn’t be a problem. Being able to communicate effectively, hierarchically — not just from a people management perspective, but also hierarchically from a chip and project management perspective — is difficult. The teams that do this well invest resources into that process, specifically the communication of top-down tightening of budgets or top-down floorplan constraints. These are important to think about because every engineer is looking at chip-level timing reports, but the problem that they’re trying to solve might not ever be visible. But if they have a report that says, ‘Here is your view of what your problems are to solve,’ you can make some very effective work.”

Further Reading
EDA Pushes Deeper Into AI
AI is both evolutionary and revolutionary, making it difficult to assess where and how it will be used, and what problems may crop up.
Optimizing EDA Cloud Hardware And Workloads
Algorithms written for GPUs can slice simulation time from weeks to hours, but not everything is optimized or benefits equally.

The post AI For Data Management appeared first on Semiconductor Engineering.

The amount of data being collected, processed, and stored in vehicles is exploding, and so is the value of that data. That raises questions that are still not fully answered about how that data will be used, by whom, and how it will be secured.

Automakers are competing based on the latest versions of advanced technologies such as ADAS, 5G, and V2X, but the ECUs, software-defined vehicles, and in-cabin monitoring also demand more and more data, and they are using that data for purposes that extend beyond just getting the vehicle from point A to point B safely. They now are vying to offer additional subscription-based services according to customers’ interests, as various entities, including insurance companies, indicate a willingness to pay for information on drivers’ habits.

Collecting this data can help OEMs gain insights and potentially generate additional revenue. However, gathering it raises privacy and security concerns about who will own this massive amount of data and how it should be managed and used. And as automotive data use increases, how will it impact future automotive design?

Fig. 1: Connected vehicles rely on software to communicate between vehicles and the cloud. Source: McKinsey & Co.

“Much of the data generated in the vehicle will have immense value to OEMs and their partners for analyzing driver behavior and vehicle performance and for developing new or enhanced features,” said Sven Kopacz, autonomous vehicle section manager at Keysight Technologies. “On the other hand, the privacy of data use can be viewed as a risk to some. But the real value – as already implemented and used by Tesla and others – is the constant feedback to improve those ADAS algorithms, enable a CI/CD DevOps software development model, and allow the rapid download of updates. Only time will tell if law enforcement and the courts will demand this data and how lawmakers will respond.”

Types of data generated
According to Precedence Research, the global automotive data market size will grow from $2.19 billion in 2022 to $14.29 billion by 2032, with many types of data collected, including:

• Autonomous driving: Data on all levels, from L1 to L5, including that collected from the multiple sensors installed on vehicles.
• Infrastructure: Remote monitoring, OTA updates, and data used for remote control by control centers, V2X, and traffic patterns.
• Infotainment: Information on how customers are using applications, such as voice control, gesture, maps, and parking.
• Connected information: Information on payment to third-party parking apps, accident information, data from dashboard cameras, handheld devices, mobile applications, and driver behavior monitoring.
• Vehicle health: Repair and maintenance records, insurance underwriting, fuel consumption, telematics.

This information may be useful for future automotive design, predictive maintenance, and safety improvements, and insurance companies are expected to be able to reduce underwriting costs with more comprehensive information on accidents. Based on the information collected, OEMs should be able to design more reliable and safer cars, and to stay in close touch with customer wants. For example, experiments can be conducted to gauge customer demand for subscription-based services such as automatic parking and more sophisticated voice input and commands.

“Diagnostic data for service and repair has been a core of automotive data analytics for decades,” noted Lorin Kennedy, senior staff product management manager for SLM in-field analytics at Synopsys. “With the advent of connected vehicles and advanced machine learning (ML) analytics, which enable a greater quantity of data to be routinely processed, this data has gained exponentially in value. As data drives feature enhancements such as mobile-like experiences and advanced driver assist capabilities, OEMs increasingly need to better understand the dependability and reliability of the semiconductor systems powering these new features. The collection of monitoring and sensor data from electronic components and the semiconductors themselves will be a growing diagnostic data requirement across all types of automotive technologies like ADAS, IVI, ECUs, etc. to ensure quality and reliability on these more advanced nodes.”

Anticipated updates to ISO 26262 regulations regarding the application of predictive maintenance to hardware, identifying degrading intermittent faults caused by silicon aging, and over-stress conditions in the field are areas to be addressed, as well. Those can include silicon lifecycle management (SLM) technologies, which can deliver more comprehensive knowledge about the health and remaining useful life of silicon as it ages.

“That knowledge, in turn, will enable service updates and future OTA releases that leverage additional semiconductor compute power,” Kennedy said. “Overall fleet performance will benefit, and the semiconductor and system design process will, too, as new insights help achieve greater efficiencies. OEM, Tier One, and semiconductor supplier collaboration on what the data brings to light – from silicon to software system performance – will enable vehicles to meet the functional safety design parameters that are becoming increasingly crucial in advanced electronics.”

Still, for data generated in vehicles, OEMs will need to prioritize which data can provide value for drivers immediately, and which data should be sent to the cloud via 5G connections.

“Tradeoffs between on-board processing to reduce data volume and data transmission network costs will likely dictate prioritization,” Keysight’s Kopacz said. “For example, camera, lidar, and radar sensor data for ADAS applications may have value for training ADAS algorithms, but the volume of raw data will be very costly to transmit and store. Likewise, driver attention data can have high value in UI design, and would be best gathered in a meta-data form. V2X data has a relatively lower data volume and should ultimately be a key data source for ADAS, providing in-car non-line-of-sight visibility of other vehicles, road infrastructure, and road conditions. Sharing this over V2N links can enable effective safety applications, but angle random walk (ARW) sensor data needs to be considered more carefully due to its complex nature. Infotainment streaming content into the vehicle also can be a valuable revenue stream for OEMs, and the content providers as well, as network operators working together.”

Impacts on automotive cybersecurity
As vehicles become more autonomous and connected, data use will increase, and so will the value of that data. This raises cybersecurity and data privacy concerns. Hackers want to steal personal data collected by the vehicles, and can use ransomware and other attacks to do so. The idea of taking control of vehicles — or worse, stealing them — also attracts hackers. Techniques used include hacking vehicle apps and wireless connections on the vehicles (diagnostics, key fob attacks and keyless jamming). Protecting data access, vehicles, and infrastructure from attacks is increasingly important and challenging.

Cybersecurity risks increase with software-defined vehicles. Memory especially will need to be safeguarded.

“The integration of advanced technology into EVs poses significant cybersecurity challenges that demand immediate attention and sophisticated solutions,” said Ilia Stolov, center head of secure memory solution at Winbond. “Central to the digital fortresses within modern electronic platforms are flash non-volatile memories, housing invaluable assets like code, private data, and company credentials. Unfortunately, their ubiquity has rendered them attractive targets for hackers seeking unauthorized access to sensitive information.”

Stolov noted that Winbond has been actively working to secure flash memory from hacks.

Additionally, there are important considerations in securing memory designs, such as:

• DICE root of trust: The Device Identifier Composition Engine (DICE) should be used to create the secure flash root of trust for hardware security. This secure identity forms the basis for building trust in the hardware. Other security measures can therefore rely on the authenticity and integrity of the boot code, protecting against firmware and software attacks. The initial boot process and subsequent software execution are based on trusted and verified measurements, helping prevent the injection of malicious code into the system.
• Code and data protection: Protecting code and data is crucial for maintaining system-wide integrity. Unauthorized modifications to code or data can lead to malfunctions, system instability, or the introduction of malicious code, compromising the hardware’s intended functionality or exploiting system vulnerabilities.
• Authentication protocols: Authentication is a fundamental and crucial component of cybersecurity, serving as the frontline defense against unauthorized access and potential security breaches. Employing authentication protocols to restrict access to authorized actors and approved software layers only using cryptography credentials is important.
• Secure software updates with rollback protection: Regular updates extend beyond bug fixes including remote firmware over-the-air (OTA) updates, guards against rollback attacks, and ensures the execution of only legitimate updates.
• Post-quantum cryptography: Anticipating the post-quantum computing era to include NIST 800-208 Leighton-Micali Signature (LMS) cryptography safeguards EVs against the potential threats posed by future quantum computers.
• Platform resiliency: Automatic detection of unauthorized code changes enables swift recovery to a secure state, effectively thwarting potential cyber threats. Adhering to NIST 800-193 recommendations for platform resiliency ensures a robust defense mechanism.
• Secure supply chain: Guaranteeing the origin and integrity of flash content throughout the supply chain, these secure flash devices prevent content tampering and misconfiguration during platform assembly, transportation, and configuration. This, in turn, safeguards against cyber adversaries.

Considering the transition to SDVs and connected cars, data vulnerability becomes even more significant.

“Depending on where data resides, different protection measures are in place,” said Keysight’s Kopacz. “Intrusion detection systems (IDS), crypto services, and key management are becoming standard solutions in vehicles. Especially sensitive data for safety features needs to be protected and verified. Thus, redundancy becomes more relevant. With SDVs, the vehicle software is constantly updated or changed throughout the entire vehicle life cycle. Ever-evolving cyber threats are particularly challenging. Accordingly, the entire vehicle software must be continuously checked for new security gaps. OEMs are going to need comprehensive testing solutions to minimize security threats. This will need to include the cybersecurity testing of the entire attack surface, covering all vehicle interfaces – wired vehicle communication networks such as CAN or automotive Ethernet or wireless connections via Wi-Fi, Bluetooth, or cellular communications. OEMs will also need to test the backend that provides over-the-air (OTA) software updates. Such solutions can reduce the risk of damage or data theft by cybercriminals.”

Data management and privacy concerns
Another issue to be resolved is how the massive amount of data collected will be managed and used. Ideally, data will be analyzed to yield commercial value without causing privacy concerns. For example, infotainment platform data might reveal what types of music are most popular, helping the music industry to improve marketing strategies. Who will monitor the transfer of such data, though? How will customers be made aware of the data collection? And will they have an opportunity to opt out of having their data sold?

As with airplanes, vehicle black boxes are installed to record information for analysis of the data after an accident occurs. The information recorded includes vehicle speed, the braking situation, and the activation of air bags, among other things. If an accident occurs resulting in a fatality, and the data from ADAS and ECU uncover vulnerability in the designs, could that data be used as evidence in court against manufacturers or their supply chains? Armed with this information, the insurance industry may decline claims. Would one or more manufacturers of the ADAS/ECU be required to hand over the data when ordered by the authorities?

“Quality requirements for sophisticated electronic parts will continue to become more rigid and strict, allowing only a few defective parts per billion (DPPB) due to the impact failed components can have on the safety and well-being of human life,” noted Guy Cortez, senior staff product management manager for SLM analytics at Synopsys. “SLM data analytics will continue to play a substantial role in the health, maintainability, and sustainability of these devices throughout their life within the vehicle. Through the power of analytics, you can do proper root cause analysis of any failed device (e.g., return merchandise authorization, or RMA). What’s more, you will also be able to find ‘like’ devices that ultimately may exhibit similar failed behavior over time. Thus empowered, you can proactively recall these like devices before they fail during operation in the field. Upon further analysis, the device(s) in question may require a design re-spin by the device developer in order to correct any identified issue. With a proper SLM solution deployed throughout the automotive ecosystem, you can achieve a higher level of predictability, and thus higher quality and safety for the automotive manufacturer and consumer.”

OEM impact
While modern cars have been described as computers on wheels, they are now more like mobile phones on wheels. OEMs are designing cars that do not skimp on features. Semi-autonomous driving, voice-controlled infotainment systems, and the monitoring of many functions—including driver behavior— are yielding a large amount of data. While that data can be used to improve future designs. OEMs’ approaches to security and privacy vary, with some offering stronger security and privacy protection than others.

Mercedes-Benz is paying attention to data security and privacy, and is compliant to UN ECE R155 / R156, a European norm for cybersecurity and software update management systems, according to the company. Which data is processed in connection with digital vehicle services depends on which services the customer selects. Only the data required for the respective service will be processed. Additionally, the “Mercedes me connect” app’s terms of use and privacy information make it transparent for customers to see what data is needed for and how it is processed. Customers can determine which services they want to use.

Hyundai indicated it would follow a user-centric focus, prioritizing safety, information security, and data privacy with fault-tolerant software architectures to enhance cybersecurity. Hyundai Motor Group’s global software center, 42dot, is currently developing integrated hardware/software security solutions that detect and block data tampering, hacking, and external cyber threats, as well as abnormal communication using big data and AI algorithms.

And according to the BMW Group, the company manages a connected fleet of more than 20 million vehicles globally. More than 6 million vehicles are updated over-the-air on a regular basis. Together with other services, more than 110 terabytes of data traffic per day are processed between the connected vehicles and cloud-backend. All BMW vehicle interfaces permit consumers to opt in or out of various types of data collection and processing that may happen on their vehicles. If preferred, BMW customers may opt out of all optional data collection relating to their vehicles at any time by visiting the BMW iDrive screen in their vehicle. Additionally, to completely stop the transfer of any data from BMW vehicles to BMW services, customers can contact the company to request that the embedded SIM on their vehicles be disabled.

Not all OEMs hold the same philosophy on privacy. According to a study on 25 brands conducted by the Mozilla Foundation, a nonprofit organization, 56% will share data with law enforcement in response to an informal request, 84% share or sell personal data, and 100% earned the foundation’s “privacy not included” warning label.

More importantly, are customers educated or informed on the privacy issue?

Fig. 2: Once data is collected from a vehicle, it can go to multiple destinations without the knowledge of customers. Source: Mozilla, *Privacy Not Included.

Applying data to automotive design in the future
OEMs collect many different types of automotive data in relation to autonomous driving, infrastructure, infotainment, connected vehicles, and vehicle health and maintenance. The ultimate goal, however, is not just to compile massive raw data; rather, it is to extract value from it. One of the questions OEMs need to ask is how to apply technology to extract information that is really useful in future automotive design.

“OEMs are trying to test and validate the various functions of their vehicles,” said David Fritz, vice president of virtual and hybrid systems at Siemens EDA. “This can involve millions of terabytes of data. Sometimes, a huge portion of the data is redundant and useless. The real value in the data is, once it gets distilled, that it’s in a form where humans can relate to the meaning of the data, and it also can be pushed into the systems while they’re being developed and tested and before the vehicles are even on the ground. We’ve known for quite some time that many countries and regulatory bodies around the world have been collecting what they call an accident database. When an accident occurs, the police show up on the scene collecting relevant data. ‘There was an intersection here, a stop sign there. And this car was traveling in this direction roughly this many miles an hour. The weather condition is this. The car entered the intersection in the yellow light and caused an accident, etc.’ This is an accident scenario. Technologies are available to take those scenarios and put them in a standard form called Open Scenario. Based on the information, a new set of data can be generated to determine what the sensors would be seeing in those accident situations, and then push it through both a virtual version of the vehicle and environment and in the future, and push those scenarios through the sensors in this physical vehicle itself. This is really the distillation of that data into a form that a human can wrap their mind around. Otherwise, you could collect billions of terabytes of raw data and try to push that into these systems, and it wouldn’t actually help you any more than if someone were sitting in a car and dragging those for billions of miles.”

But that data also can be very useful. “If an OEM wants to obtain safety certification, say in Germany, the OEM can provide a set of data of scenarios on how the vehicle will navigate,” Fritz said. “An OEM can provide a set of data to the German authority, with a set of scenarios to prove the vehicle will navigate in a safe manner under various conditions. By comparing that with the data in the accident database, the German government can say that as long as you avoid 95% of the accidents in that database, you’re certified. That’s actionable from the perspectives of human drivers, insurance, engineering, and visual simulation. The data prove the vehicle is going to behave as expected. The alternative is to drive around, as in the case of autonomous vehicles, and try to justify the accident was not caused by the vehicle, while facing the lawsuit. It does not seem to make sense, but that’s what’s happening today.”

Related Reading
Curbing Automotive Cybersecurity Attacks
A growing number of standards and regulations within the automotive ecosystem promises to save developments costs by fending off cyberattacks.
Software-Defined Vehicles Ready To Roll
New approach could have big effects on cost, safety, security, and time to market.

The post Increased Automotive Data Use Raises Privacy, Security Concerns appeared first on Semiconductor Engineering.

The challenges before semiconductor fabs are expansive and evolving. As the size of chips shrinks from nanometers to eventually angstroms, the complexity of the manufacturing process increases in response. It can take hundreds of process steps and more than a month to process a single wafer. It can subsequently take more than another month to go through the assembly, testing, and packaging steps necessary to get to the final product.

Artificial Intelligence (AI) can be deployed within a fab to address the complexity and intricacy of semiconductor manufacturing. A fab generates petabytes of data as wafers go through the multitude of process and test operations. This wealth of data also presents a challenge in that it needs to be analyzed and acted on quickly to ensure tight process control, high yield, and avoid process excursions. Beyond navigating the complexity of the manufacturing process, new solutions are necessary to help make the process as efficient as possible and the yield as high as possible to produce the most business value for fabs.

The benefits of AI-enabled analysis tools for IC manufacturers

Traditional techniques to detect issues in the manufacturing process have run out of steam, especially at advanced technology nodes. For example, an engineer must do their own yield analysis to seek out potential problems. Once they identify an issue, they communicate with the defect and process teams to determine the root cause and then troubleshoot it. The defect team will begin work to find some correlation behind the issue and the process team troubleshoot and link it to the root cause.

All these steps take up significant time that could be focused on achieving the highest yield of chips possible, driving costs down and reducing time to market. One of the biggest benefits of enabling AI in analysis tools is that an engineer can quickly recognize and pinpoint an issue in a specific chip to see which process step and/or equipment has caused the issue.

Beyond the fast and accurate process control that AI allows for, there are numerous other benefits that result from the saved time and money, including:

• Predictive applications: Enables fabs to take leap from reactive to predictive process control
• Scalability: Analyzes petabytes of data, connects multiple fabs, and comes cloud-ready
• Efficiency: Allows fab to make better decisions and reduce false alarms

To enable the next generation of manufacturing, Synopsys is enabling AI and Machine Learning (ML) for a comprehensive process control solution.

Actionable insights with AI and ML

Wafer, equipment, design, mask, test, and yield are silos within a fab that can benefit from a comprehensive AI/ML enabled solution. Such a solution can specifically help engineers generate actionable insights into the following:

• Fault detection and classification (FDC)
• Statistical process control (SPC)
• Dynamic fault detection (DFD)
• Defect classification and image analytics
• Defect image analytics
• Decision support system (DSS)

Fast analysis of petabytes of data, from equipment sensors or process parameters, allows manufacturers to quickly identify the root cause of process excursions and take action to maintain yield.

AI and ML in the fab

Synopsys is a provider of software solutions for silicon manufacturing and silicon lifecycle management, including solutions for TCAD, mask solutions, and manufacturing analytics. Its existing solutions are connected to thousands of pieces of equipment over multiple fabs with millions of sensors, analyzing hundreds of petabytes of data. By providing real-time visibility into the manufacturing process, Synopsys enables predictive analytics and optimizes product quality and yield to help give semiconductor fabs a leg up in this competitive landscape.

Synopsys has introduced an AI/ML enabled software offering, Fab.da, to make semiconductor manufacturing efficient. Fab.da is a part of the Synopsys EDA Data Analytics solution, which brings together data analytics and insights from the entire chip lifecycle

It offers a complete data continuum by bringing together these different data types from many different sources into one platform for both advanced and mature node chips. This data continuum allows for high user productivity, maximum data scalability, and increased speed and accuracy in root cause analysis for issues.

Delivering process control solutions to manage complexity at leading-edge fabs, Fab.da can help chip designers and manufacturers drive operational excellence and productivity, providing a competitive edge in today’s manufacturing landscape.

The post Utilizing Artificial Intelligence For Efficient Semiconductor Manufacturing appeared first on Semiconductor Engineering.