machine learning

New system-on-chip (SoC) devices are driving new memory architectures and photonic interfaces, while specialized new intellectual property (IP) requires analysis down to the nanometer and atomic levels because of single nanometer process nodes. According to Babak Taheri, CTO and EVP of products at Silvaco, a leading EDA Software, semiconductor IP company, a member of SEMI and the ESD Alliance, a SEMI Strategic Association Partner, design technology co-optimization and proven IP are required for this analysis.Taheri recently discussed atoms to systems in next-generation SoC designs with Nanette Collins ahead of ES Design West, co-located with SEMICON West, July 9-11 at the Moscone Center in San Francisco.ESD Alliance: For years now, the assumption is that each new chip design is more complex than the last. Why are the latest SoC designs even more complex than before?Taheri: New SoC devices for mobile phones, automobiles, intelligent edge nodes, big data compute and storage are adopting artificial intelligence and machine learning technologies. This is driving new compute, data flow, as well as memory architectures that are bandwidth-limited and some require photonic interfaces.One common denominator in present SoC design are the numerous blocks of IP. On average, over 85% of these blocks are reused. It’s cost-prohibitive to make these chips over and over again with new IP. According to some estimates, 90% of IP used in an SoC design by 2025 will be reused – only 10% is new technologies. That 10% is significant.ESD Alliance: How so?Taheri: Complex new technologies including flash memory, other advanced non-volatile memory technologies such as MRAM, RRAM and SoCs such as NVIDIA’s Xavier and Apple’s A12 use and reuse design IP at the architectural level.New technologies mean new materials and new processes. Single nanometer process nodes require specialized new IP that needs to be simulated and analyzed down to the nanometer and atomic levels.ESD Alliance: Does the atomic level changes the design equation?Taheri: Yes, it does. Designers need to be able to simulate at the atomic level and understand properties of these materials, and how they behave in at-process and at-device levels. They need be able to simulate the material's nanometer geometries, how molecules behave and how they interact for device operations. When they put together a process and a device, they need to know how the pieces behave and simulate before production.In other words, they run quite a few design experiments and quite a bit of simulation before they finalize the circuits and devices to silicon to save money.ESD Alliance: It’s obvious design automation will continue to have a vital role in design.Taheri: Yes, absolutely. Design technology co-optimization (DTCO) using TCAD solutions and proven design IP are needed to address the span from architecture to device and process physics. The importance of simulation, emulation and design technology co-optimization, along with fully verified and proven IP for SoC design, cannot be overstated. As designers generate devices and processors, they take that up to circuit-level simulation and high-level simulation, schematic capture, extractions and back annotation. They can go from atoms to simulating systems to the ability to do that under the same umbrella in order to get better chips, better yield and lower cost.Taheri’s talk Next Generation of SoC Design: From Atoms to Systems will be part of the Meet the Experts More than Moore session Tuesday, July 9, at 11:30 a.m. at the ES Design West SMART Design Pavilion. SEMICON West attendees are invited to Moscone Center’s South Hall to learn more about electronic system and semiconductor design and its links to the electronic product manufacturing and supply chain. Register for ES Design West or SEMICON West.Babak Taheri is Silvaco’s CTO and EVP of products, has more than 25 years of design experience. His current role managing Silvaco’s Technology CAD (TCAD), electronic design automation (EDA) and IP product divisions makes him an expert on what’s needed for the design of next-generation system-on-chips (SoCs). Previously, he was the CEO and president of IBT working with investors, private equity firms, and startups on M A, technology and business diligence. Babak received his Ph.D. in biomedical engineering from the University of California Davis with Bachelor of Science degrees in Electrical Engineering and Computer Science and Neurosciences. He has published more than 20 articles and holds 28 issued patents.Nanette Collins is a public relations representative for the Electronic System Design Alliance.

On the day I joined SEMI in March of 2017, I was filled with excitement to be on-boarding at a time when great, leaping strides in innovation were driving the rapid expansion of our ecosystem. In my many conversations with members that followed, I was not surprised that a vast majority ranked among their top concerns the persistent challenge of attracting, training and retaining the talent needed to grow their businesses. Later that year, I raised the global talent shortage issue in my article Securing Talent to Connect, Collaborate and Innovate. As an industry veteran I knew that the decades-long workforce development challenge will only worsen with the proliferation and increasing complexity of technology.Innovation has never been more technology-intensive. Developing the technology and producing the components required for applications powering next-generation communications (5G), artificial intelligence (AI) and machine learning, autonomous vehicles, and the Internet of Things (IoT) require bright minds in diverse fields of science to fill critical positions in the global electronics manufacturing industry. Today, that talent struggle is acute, threatening to undermine our industry’s potential to grow to $1 trillion by 2030.The electronics industry needs a comprehensive, integrated program to build the talent pipeline. The program should inspire school-age children to adult learners to pursue careers in this great but underrecognized industry. It needs to shine a spotlight on career opportunities. It must prepare workers with standardized skills sets transferable across the industry. And it must connect trained workers with hiring companies.SEMI is uniquely positioned to deliver this solution. Launched almost two years to the day after I joined SEMI, SEMI Works is SEMI’s branded workforce development initiative. We realize that trade associations don’t create jobs. Their members do. Think of SEMI Works as SEMI’s commitment to build and maintain the needed infrastructure – the talent pipeline. SEMI Works is comprehensive. The program, supported by SEMI members, is a wide-ranging effort by our Global Advocacy team to ensure education is demand-driven, training programs better meet the needs of the industry, more people pursue careers in electronics and our members have access to the talent pool that we are cultivating. With SEMI Works, SEMI is developing scalable solutions to improve connections among training and education providers, prospective workers and the industry. Key features of SEMI Works will include SEMI-certified education courses and training programs linked to industry requirements and skills credentialing for workers.SEMI Works starts with raising awareness of SEMI-certified programs as a key bridge connecting prospective talent, the industry and applicable training and education programs. Growing awareness of the programs will enable SEMI to build an extensive database of employers and qualified talent and link both to the right training. SEMI will continue to drive and endorse programs that help meet member needs throughout the education continuum – from K-4 to higher education and adult training. But the infrastructure and ecosystem required to support and scale these programs is the key for all of us to win together. At a high level, SEMI Works consists of several important components: Linking the required industry competencies to education and training course curriculum – Similar to the establishment of SEMI standards, SEMI will certify education and training programs that dovetail with the industry competency model. Initial certification and annual re-certification ensure continued updates, relevance and sustainability of the programs. SEMI will raise awareness of SEMI Works certified programs as the standard for meeting the industry’s talent requirements. Developing and maintaining the electronics industry competency model – Through established working groups and ongoing dialogue with our members, we are developing a competency model – a tiered matrix of required competencies used to link course curriculum to the talent needs of employers. The competency model consists of interpersonal and individual skills, academic and general industry requirements, advanced manufacturing competencies, and competencies by job. SEMI will establish and maintain the model with regular updates. Improving access to talent – Through SEMI Works, SEMI will build an extensive database that brings together programs, talent and employers. People and organizations opting into a SEMI-certified program or acquiring a SEMI program certification will be part of the SEMI database. Job seekers will be able to set up a profile and resume and search for training and employment opportunities, and employers will search the talent pool – much as job-search sites work today – assured of a skills match based on the SEMI certification. I am passionate about education and proud of all of SEMI’s efforts. I am especially proud of the work we are doing to help provide a pathway to meaningful careers for children and adults all around the world. We no longer have the luxury of a piecemeal approach to training and education.It is my hope and belief that SEMI Works, together with our efforts to improve diversity and inclusion in the workforce, will be SEMI’s lasting mark on the global electronics industry.Ajit Manocha is president and CEO of SEMI.

MedTech, autonomous driving and other disruptive technologies will be in focus at the SEMI Industry Strategy Symposium (ISS Europe), 31 March - 2 April 2019 in Milan, Italy, as top European executives, researchers and academics gather to explore solutions to the region’s most pressing strategic, economic and social challenges. Ahead of ISS Europe, SEMI spoke with Mark Purdy, managing director and chief economist at Accenture Research, about Accenture’s Business Futures – four different future worlds set in 2025 based on the collision of trends across demographics, geopolitics, technology, and economics – and what these futures will mean for markets, workforces, operating models and industry value chains. SEMI: At ISS Europe in Milan, you will kick off the symposium highlighting market opportunities of the digital economy and how companies must adapt to competitive challenges. What inspired Accenture’s Business Futures four world scenarios?Purdy: The impetus for our Business Futures really stemmed from a certain dissatisfaction with current approaches to thinking about the future. We were struck by the following puzzle. First, there is no shortage of techniques for looking at the future, from forecasting to trends analysis to conventional scenarios. Second, most decision-makers have more or less the same access to information on global trends. Yet, time and again, we hear stories of businesses going bust or facing major challenges precisely because they failed to anticipate major changes in their industry.The paradox is that we have so much information, but so little real understanding of how the future actually unfolds. So that set us thinking about how to develop a new approach, based on a combination of detailed trend analysis, expert input and creative storytelling – which is what we call “Business Futures.” SEMI: Of demographics, geopolitics, technology, and economics, which trend do you see as particularly critical?Purdy: Actually, the essence of our Business Futures thinking is that it is the collision or combination of different trends – across economics, technology, demography, etc. – that shapes future outcomes, rather than individual trends per se. To a certain extent we tend to become fixated on specific trends and this can lead us astray or cause bad decision-making. For example, in the early 2000s many people saw very favorable trends in the U.S. economy – strong capital inflows, rapidly rising consumer spending, surging stock markets, and rising home ownership rates. Each trend in isolation looked strong and sustainable. But we failed to see how the combination of these trends was fueling risky financial innovation that would eventually lead to the financial crisis and great recession.Technology of course is a key trend. We are seeing tremendous advances in next-wave technologies such as robotics, machine learning, intelligent objects, 5G and virtualization. But we can only truly understand the impact of the technologies – and the business opportunities and challenges they create – by viewing them against a wider backdrop of changes in society, demography, geopolitics and economics. That is what Business Futures strives to do.SEMI: What will these different futures mean for markets, workforce, operating models and industry value chains?Purdy: There will be profound changes in how we think about all of these areas. Markets will become much more personalized and interactive. Technology will be increasingly integrated with humans, fueling innovation in areas such as personalized healthcare and preventative medicine. Our notions of distance and capacity will be upended, as new virtualized services enable new ways of reaching underserved customers. Consumers will become increasingly involved in the creation and design of products and services. New methods of innovation, powered by AI and virtualization, will come to the fore. New entrants will come from unexpected quarters, enabled by new technology. The upshot will be massive disruption and disintermediation of value chains across many sectors.SEMI: What can Europe do to prepare?Purdy: There are no simple answers, and the correct course will vary by country, but there are some basic things to get right. First, different countries need to understand their comparative advantage – for example, whether it is in services, new technologies, advanced manufacturing or resources – and work with the grain of these different futures. Second, countries need to ensure that they have the basic conditions – regulation, organizational adaptability, workforce flexibility, skills, and innovation infrastructure – to capitalize on the productive potential of new technologies such as AI, virtual reality, and the Internet of Things (IoT). Third, we need to create educational systems and workforce learning methods that emphasize creativity, problem solving and innovation – precisely the skills that will be most needed in an age of intelligent machines. SEMI: What are your expectations for the summit in Milan and for the future?Purdy: I’m very much looking forward to the ISS Europe Summit in Milan. As an economist, I believe we are at a pivotal moment in the semi-conductor industry, driven by waves of technological change and rising geopolitical frictions and uncertainty. With so many industry leaders and experts coming together at the Summit, I’m confident that our discussions will help point a way forward!Mark Purdy is managing director of economic research at Accenture Research. His research examines issues at the intersection of economics, technology and business. He has published widely in tier-1 media and specialised publications on topics such as China’s economy, emerging-market geographic strategy, inclusive economic growth, business futures and the economic impact of new technologies such as the Internet of Things and artificial intelligence. A graduate of Trinity College Dublin, he speaks on these topics at conferences and seminars around the world.Serena Brischetto is a marketing and communications manager at SEMI Europe.

For nearly two decades, Sean Ding, CTO and chief scientist of Alibaba Cloud IoT, has worked in software and algorithm architectures, sensing, semiconductors, systems and cloud computing – all areas that have contributed to the rise of the Internet of Things (IoT). It’s no surprise, then, that Alibaba is leading next-generation innovation for the IoT. Ding will bring his expertise to his role as moderator of Brave New World - MSIG Conference on AI+IoT 2019, a half-day forum March 20, 2019, at SEMICON China in Shanghai, China. Maria Vetrano of SEMI spoke with Ding about technologies key to the IoT era including MEMS, sensors, artificial intelligence (AI), edge gateways and cloud computing. SEMI: MEMS sensors are widely used in IoT devices. What is the relationship between AI and MEMS sensors?DING: While MEMS sensors and AI will increasingly co-reside in end-user devices, I do not recommend adding AI next to the sensor (in the same package). That’s because designers continue to use the ASIC for signal conditioning, so A/D converters are still required. Rather, we should look to edge gateways to carry the majority of the workload, including deep learning, because this reduces system complexity and power consumption.SEMI: Why are smarter sensors shifting data processing and analytics to the edge of IoT devices?DING: Data processing and analytics are very important for IoT devices, but we need to focus on understanding the data, parameter calibration and more. The MEMS sensor industry should leave big data analytics to edge computing and cloud computing because AI requires deep learning, demanding a huge amount of data.The challenge is to find the sweet spot for data processing right next to the sensor element.SEMI: What is China’s evolving role in innovation in MEMS sensors for IoT devices?DING: At present, the MEMS community in China needs to figure out how to innovate instead of copying existing technologies, a low-margin business that will not help to grow the industry. One reason why I am so pleased to see the MSIG Conference on AI+IoT in China is that it will encourage greater creativity in the MEMS community in China, and this will ultimately lead to Chinese companies and R D institutions leading innovation rather than copying it.SEMI: What is the right approach to combining smart MEMS sensors with AI in IoT devices? Why is this important for both domestic Chinese and international markets?DING: Combining data from sensors with cloud-edge computing is the right approach. As sensor companies increasingly provide end-to-end solutions, such as “sensor+ firmware + SaaS + app,” we will realize easier and faster integration of sensors in IoT applications.This is incredibly important because China today is the world’s biggest market for IoT hardware. China has 2,000-plus design houses, 200-plus OEMs and thousands of distributors. That said, we still see a highly fragmented market that will benefit from a faster integration methodology.Faster integration of MEMS sensors and AI/machine learning for IoT hardware will benefit designers in international markets as well.SEMI: What do you hope MISG Conference on AI+IoT attendees will take away from the forum? DING: MEMS sensors are highly fragmented, reflecting the highly fragmented applications in which they play. The MEMS sensors industry should figure out how to provide one-stop-shopping solutions for vertical markets. This will speed the scalability of applications and expedite the growth of sensor production. Sean Ding (柯镇) will moderate Brave New World - MSIG Conference on AI+IoT 2019 at SEMICON China on Wednesday, March 20, 2019, at Kerry Hotel Pudong in Shanghai, China.This conference has been organized by the MEMS Sensors Industry Group (MSIG). Register today to connect with Sean Ding and featured speakers at the event.Speakers at the MSIG Conference on AI+IoT 2019 at SEMICON China include: Welcome and Introduction / 欢迎辞Carmelo Sansone, Director, MEMS Sensors Industry Group (MSIG), a SEMI technology community AI Needs Accurate Data – MEMS Sensors Can Provide It / MEMS传感器为人工智能提供真实数据Andrea Onetti, Group VP of Analog MEMS Group, GM of MEMS Sensor Division, STMicroelectronics Enhanced IoT Edge by Smart Sensors / 智能传感器助力IoT边缘智Bennini Fouad, Regional President Asia Pacific, Bosch Sensortec Horizon AI Processor Solution, Enable Industries in AI Time / 地平线AI芯片解决方案，赋能千万业Carl Zhang 张永谦, General Manager/VP, Smart Chip Solutions Division, Horizon Robotics Inertial Sensors in AI Applications / 运动传感器AI应用案例Ben Lee 李彬 , CEO, mCube Ultra-Low-Power Solutions: an Ecosystem Approach / 超低功耗的生态链解决方案Carlos Mazure, IEEE Fellow, Chairman Executive Director, SOI Industry Consortium High-Integrity, Fault-Tolerant Open Inertial Measurement Platform for AI-based Vehicle Automation / 适用于人工智能车辆自动控制的高集成及容错的惯性测量开放平台Dan Dempsey, Senior Director of Automotive, ACEINNA Maria Vetrano is a public relations consultant at SEMI.

Technologies promising huge growth such as Artificial intelligence (AI), 5G, machine learning, high-performance computing, and telematics are ratcheting up pressure on semiconductor manufacturers in the race among product makers to accelerate time to market and capture share. To support rapidly evolving end markets for these and other technologies that are key drivers of industry growth, chipmakers are boosting semiconductor performance, producing more wafer sizes and improving manufacturing efficiency.At the same time, chip manufacturers must enable unprecedented end-product reliability for exploding markets such as automotive and healthcare markets where, with lives at stake, products can’t afford even the slightest lapse in reliability. In response, chip suppliers are retooling their manufacturing processes to support 3D stacking, package-level integration and miniaturization. But they must do more. Bringing high efficiency to all phases of manufacturing including design and materials is the new imperative. The key to quality management is not in the traditional post-production testing and damage control but in prevention. Delivering the highest quality and reliability must start in the earliest stages of production with manufacturing and testing design – an approach that reduces not only the cost of downstream testing but minimizes product defects that can damage a supplier’s credibility and lead to lost business.To that end, SEMI has launched its Quality Assurance Special Interest Group (SIG) consisting of representatives from industry leaders such as Infineon, NXP, TSMC, UMC, ASE, Unimicron, and GCE. The group's goal is to establish quality requirements spanning the supply chain to meet new, higher reliability standards and help safeguard Taiwan’s competitive edge in the global microelectronics industry. Meeting for the first time earlier this month, the companies exchanged ideas for improving quality management in semiconductor manufacturing and ultimately deliver the reliability the market needs.The company representatives unanimously agreed that the first step is to ensure a QA-friendly environment with quality requirements for various stages of chipmaking ranging from design, manufacturing, packaging and testing to even PCB and CCL production. The SEMI Quality Assurance SIG this year plans to build on its current membership by enlisting companies from various fields to address critical areas of reliability including statistical process control, surface-mount-technology-based board level reliability control, and 0 dppm quality control for automotive chips. SEMI Quality Assurance Special Interest Group consists of leading companies in the industry, including Infineon, NXP, TSMC, UMC, ASE, Unimicron, and GCE. “SEMI’s comprehensive platform of exhibitions, programs, forums, trade meetings and matchmaking events is instrumental in bringing together key industry players to enhance quality management practices and meet the growing reliability requirements of the end markets we serve,” said Terry Tsao, chief marketing officer at SEMI and president of SEMI Taiwan. “The Quality Assurance Special Interest Group is a shining example of how SEMI continues to support the crucial role of Taiwan’s semiconductor industry in the international community.”For more information about the SEMI Quality Assurance Special Interest Group or to become a member, please contact Emmy Yi at [email protected] Yi is a marketing specialist at SEMI Taiwan.

SEMI has long promoted the industry collaboration that has contributed to the rise of the smart digital world we live in today. A world where data is being generated continuously by systems, gadgets, and sensors around us – often referred to as the Internet of Things (IoT). In our personal lives, most of us have smartphones, smart watches, smart TVs and smart cars, and we live in smart homes and smart cities generating huge amounts of data.In the work world, data and analytics are now influencing almost every industry including healthcare, government, financial services, construction and transportation. This data has the potential to transform our lives and make our world even smarter – if we can communicate and process this data, and use it to come up with actionable recommendations or actions. Artificial Intelligence (AI) and Machine Learning (ML) techniques have generated much excitement precisely because they offer us ways to realize the full value of data by harnessing it and transforming it into active intelligence.Data-intensive technologies are required to store, communicate and analyze data. And it all starts with innovation in microelectronics chips and systems spanning processors, memory, sensors, radios and other devices, presenting a huge opportunity to producers of these technologies. However, with Moore's Law beginning to slow, technology paths and innovation options are diverging. Companies must swiftly assess these options in order to develop competitive offerings. But the technological complexity and divergence makes it increasingly expensive or even unaffordable for many companies to track and pursue these options.The good news is that cost-effective early assessment is possible through pre-competitive collaboration that can produce new and often unexpected cross-disciplinary insights by overcoming traditional silos in industry and academia. Unfortunately, important collaborative industry platforms, such as the International Technology Roadmap for Semiconductors (ITRS), have folded, opening a collaboration gap in the global microelectronics ecosystem.As part of its mission to help companies connect, collaborate, and innovate, SEMI has built a collaborative, cross-supply-chain platform – the Strategic Innovation Platform (SIP). The goal is to provide early and comprehensive assessment of future technologies that are five to eight years away from commercialization. The assessment identifies not just technical barriers but also manufacturing and supply-chain constraints to implementing new technologies. SIP brings together the entire microelectronics ecosystem including strategic technology thought leaders, subject matter experts, technology and application developers, academia, researchers, start-ups and government. With more than 2,100-member companies spread across the global electronics manufacturing supply chain, SEMI is uniquely positioned to enable this critical collaboration. Award-Winning First ProjectThe inaugural SIP project assessed key drivers of future technologies. A key finding was that fast, efficient interconnects between devices and components are critical to the system performance important to customers and users, implying that system-level optimization is required. For data-intensive applications, interconnects have emerged as a key bottleneck for both performance and power in various circuits and systems in part because the slowing of Moore’s Law has decelerated advances in individual device performance, and in part because systems are becoming more complex, requiring heterogeneous integration.To address this challenge, SIP brought together industry experts from ASE Inc., Dow Chemical, Lam Research, Qualcomm and Xilinx to assess the future impact of interconnects for data-intensive applications. SEMI also involved Stanford University professors to collaborate on modeling and simulation. Through this unique cross-disciplinary collaboration, SIP developed a realistic model to evaluate the system-level performance of single-chip systems, as well as multi-chip systems – including traditional 2D packages, high-performance 2.5D systems that use interposers, and futuristic 3D systems. SIP also explored supply chain challenges in business continuity, manufacturability, Environment, Health and Safety (EHS) and the regulatory environment. SEMI worked with a broad range of industry partners to ensure that the model parameters accurately reflected realities on the design and factory floors to ensure usable results. Experimentation has become ever more expensive, with one industry player reporting that “it costs us $100 million to do a good experimental evaluation.” Accurate models can go a long way toward reducing the cost of technology assessment. The SIP collaboration produced key quantifiable insights including comparisons that highlight the benefits and limitations of various materials being explored for future interconnects, and of architectures under consideration for future data-intensive applications. For example, the current workhorse for artificial intelligence (AI) platforms – 2.5D technology – delivers a 4X improvement over 2D packaging but falls short of providing the orders-of-magnitude improvement that future AI/ML applications may require. These findings enable the industry to begin to identify ways to optimize 2.5D architectures, transition to 3D heterogeneous integration for performance-critical applications in the medium term, and to eventually evaluate new paradigms such as neuromorphic and quantum. The project findings were presented late last year in the form of two research papers at Electronics System-Integration Technology Conferences (ESTC) and International Microelectronics Assembly and Packaging Society (IMAPS) recently. One received the “Best Paper of the Session” award at IMAPS – a recognition that affirms the power of a collaborative platform such as SIP to produce valuable insights to address the growing technology complexity within the microelectronics industry. The microelectronics industry is on the cusp of a historic inflection point, where it could fuel the rise of emerging applications in AI/ML and IoT, and can grow into a trillion dollar industry over the next several years. More importantly, the industry is poised to help solve some of society’s most complex problems in areas including healthy living, climate change and transportation. No company can do this alone, and pre-competitive platforms such as SIP are key both to accelerating innovation through cross-disciplinary collaboration, and to reducing costs for individual companies. Please contact Tom Salmon at [email protected] or Pushkar Apte at [email protected] for more details and to get involved in future projects.Tom Salmon is vice president of Collaborative Technology Platforms. Pushkar Apte is a strategic technology advisor at SEMI.

Constant coverage of an invigorating topic like machine intelligence in the media often urges us to consider its use in EDA technology. As is often the case, there are many myths and falsehoods that consume our time and effort when trying to apply machine intelligence to EDA. This article aims to uncover the myths and to provide helpful advice on applying machine intelligence to your EDA project or product.Value PropositionFirst, there needs to be a clear value proposition for adding machine intelligence to an EDA product. Using machine intelligence to create a me-too product adds no value. EDA customers are too busy to understand or care about an EDA tool’s underlying technology. They just want to use the tool and get results. If the tool delivers value, if it delivers tangible benefits, then they’ll use it. Otherwise, they won’t.Currently, EDA tool developers are already experimenting with AI and machine intelligence without considering this fundamental truth – without a higher-end objective. AI must deliver something better or new, whether a speed advantage, a performance advantage, new features, new insights, or perhaps even something pleasantly surprising. Before you write a single line of AI-enhanced code, you need to clearly understand how AI will enhance the product. What is the value proposition?Use ModelThere’s a major barrier to customer adoption of AI and machine intelligence technology for EDA tools: EDA users are averse to make decisions based on probabilistic results. Instead, half a century of EDA tool use has conditioned them to expect deterministic outcomes from their tools.Back in 2003, a prominent visionary and EDA investor was quoted in an interview, saying: “If I open my eyes five years from now, all static analysis in VLSI will be statistical.” Many EDA luminaries have been proven wrong over time for betting that EDA users will accept statistical results. As enthusiastic as I am about using machine intelligence to improve EDA tools, I must urge caution based on the history of EDA failures that employed a probabilistic use model. Decision-makers and EDA tool users want to see deterministic answers to questions about yield or slack, not probabilistic ones.Our experiences at Paripath in developing the PASER (Paripath Accelerated Simulation Environment) tool also bear this out. We discovered that delivering results 50x faster but with 92% accuracy was simply not good enough for end users. EDA users only started to use PASER when its answers became 98+% accurate. To be adopted in the production flow, the tool had to deliver 99% accuracy.Data EngineeringThere are specific ways to achieve these accuracy goals. The first is data engineering. Machine intelligence is a new approach to EDA tool development and it needs to be trained on a data set. If the data is poor or incomplete, training will create an inaccurate model. Fundamental software-development rules still apply. Garbage in, garbage out.Without good training data, there’s no way for you to build good neural-network models. If you train a model with garbage data, you’ll get a garbage model. You must cleanse the data before you use it for training. Otherwise, the model will draw inaccurate conclusions and customers will not use your tool. The model is not to blame here. The model’s not wrong. The problem lies in poor data engineering, poor data cleansing, and a lack of discipline to prepare input data.High DimensionalityNext, machine intelligence has a unique ability to quickly solve problems of high dimensionality. Pure EDA problems often have high dimensionality. Over the years, EDA developers have perfected the art of segmenting the problems into sequencing solutions with lower dimension. Machine intelligence technology can handle problems with thousands of dimensions, but you need to be careful when tackling problems that have high dimensionality. Too many dimensions can produce confused or inaccurate results with AI and deep-learning technology.It helps to visualize the problem and to analyze the data set before using the data to train an AI-enhanced EDA tool. Several visualization methods can help. For example, t-SNE (t-Distributed Stochastic Neighbor Embedding) lets you reduce a data set’s dimensionality from a very large number to a much lower number. Figure 1 shows a high-dimension dataset with a dimensionality of 2000, which has been reduced to a low dimensionality of 3. Figure 1: Visualizing the Data Set with Lower Dimensionality Reducing the dimensionality of a data set to 3 using t-SNE and visualization allows you to quickly see whether the data set defines an easy or a difficult problem. If the problem is difficult, you’ll likely need to lower the problem’s and the data set’s dimensionality before using the data to train a neural network.Technology SelectionOne factor that determines whether it will be easy or difficult to incorporate machine intelligence into your EDA tool is your choice of AI development tools. AI researchers have developed a long list of frameworks, libraries, and languages that they use to develop AI and machine-learning software. Frameworks and libraries such as TensorFlow, Caffe and MXNet are most popular for developing deep-learning models.However, these tools are not yet popular with the EDA development community. The languages of choice in the EDA community are traditionally C and C++ for development and Tcl for prototyping and creating user interfaces. The rest of the software world has moved on to newer development languages such as Python, Java, R, and such. Moreover, machine-learning development segments into two distinct processes: training (i.e. generating the model) and inference (i.e. using the model).Another question to consider is where to generate the model – at the vendor site or the customer site?Consequently, fitting AI and deep-learning development into EDA development environments can feel like fitting a square peg into a round hole. You may need to create corners in your hole.EDA is a very small player in the overall software market. Relatively few software developers are familiar with writing EDA tools. It’s best to select AI and deep-learning development tools that can provide some sort of interface that’s compatible with EDA’s development tools of choice. Some AI frameworks have lower-level C and C++ interface layers that provide a familiar entry point for experienced EDA developers.At Paripath, we chose TensorFlow for exactly this reason. TensorFlow has a lower-level C/C++ interface. Although the resulting development path becomes a longer one using this approach, it’s a more familiar path for EDA developers and therefore it’s a path that can ultimately lead your EDA development team to success. An elaborate study of comparing these frameworks has been published in the book Machine Intelligence in Design Automation.Integration into Legacy SystemsWhen you understand the value that you expect machine intelligence to add to your new EDA tool, when you’ve cleansed and then analyzed the data set, and when you have selected an appropriate set of development tools, you’re finally ready to add machine intelligence to your EDA development. There are two use models for AI-enhanced EDA tools. The first uses a trained model to guide the EDA tool’s decision-making. In this use case, the trained neural network doesn’t change. The software’s accuracy doesn’t improve with use unless the company that developed the EDA tool retrains the underlying neural network. This use case follows the familiar, existing use case associated with EDA tools developed using deterministic algorithms.For the second use case, the end user is able to retrain the underlying neural network, which allows the EDA tool to produce better, more accurate results over time. This use case produces a win/win situation because end users are able to hone their tools and improve them over time, without help from the EDA tool vendor’s application engineers. If the retrained models are also sent back to the EDA developer for incorporation into newer versions of the tool, all users benefit from other users’ training data.It’s not clear how you’d support this second use case in the current EDA business environment where most data sets are proprietary and are carefully guarded. Most large EDA tool customers want to keep their data in house under tight control. Even with this somewhat restrictive situation, however, EDA tools benefit from the incorporation of machine intelligence because each EDA tool customer can customize the tool and improve its results.Machine intelligence has much to add to EDA tools’ capabilities. Only time will tell if the customers want and will accept these new capabilities. Rohit Sharma, founder and CEO of Paripath Inc., is an engineer, author and entrepreneur. He has published many papers in international conferences and journals. He has contributed to electronic design automation domain for over 20 years learning, improvising and designing solutions. He is passionate about many technical topics including machine learning, analysis, characterization, and modeling. It led him to architect guna - an advanced characterization software for modern nodes. Sharma has written a book titled “Machine Intelligence for Design Automation.” You can download code examples and other information here.Note from SEMI-ESD Alliance: ESD Alliance’s Interoperability Committee brings together the industry to discuss interoperability. By focusing the efforts of the electronic system design community onto key compute operating systems, the Interoperability Committee seeks to define a stable, interoperable environment for tools and streamline the resources required to support these environments. The EDA Industry OS Roadmap presents guidelines to EDA vendors and customers for compute platforms to target for design starts. Learn more and view the OS Roadmap overview at our website.

Semiconductor fabs have been getting smarter and smarter over the past 30 years. It’s a natural evolution – the direct outcome of numerous continuous-improvement efforts. The really important difference on the road to smarter fabs, the one change that’s enabling the Industry 4.0 revolution, is the concept of a cyber-physical system or digital twin. If you don’t have a thorough, detailed, high-fidelity digital twin of your entire fab operation, then you cannot have “Smart Manufacturing.” That’s really the definition of a smart site. A digital twin is simply a requirement for all smart factories of the future. One caveat: No matter what you build today from a smart perspective, your digital twin’s fidelity will improve over the next 20 years. A factory’s digital twin has two facets: the operational aspect and the yield aspect. Each of these two facets places different requirements on a database including the types of data, the frequency of data generated, the retention of data, and even the AI/ML techniques used to analyze the data. A combination of these data requirements are needed to create a digital twin – the virtual representation of your entire factory operation, whether it’s on the wafer-fab front end or the assembly and test back end. What’s most important here is that facility-wide data sets and databases must be able to communicate with each other using refined summary statistics to create a practical digital twin. For example, a lot of information is collected on the yield side to feed the deep-learning models needed to manage processes. However, the factory scheduler, driven largely by the smart operational database, needs only summary statistics from the yield database to be able to act in the next moment or over the next 24 hours. Figure 1 illustrates the needs of and the interaction between a smart operational and a yield database. Figure 1: The Operational and Yield databases in a Smart Factory need to exchange summary statistics. Today, we find that although these databases generally speak to each other in smart factories, they’re still not sufficiently connected to permit the use and analysis of data needed to realize the full potential of a smart factory. That level of interconnectedness is still in the future. Some solution providers have created what is essentially a “smart learning warehouse” (“database” has become too limited a term here). This warehouse collects, analyzes and learns from the extensive amount of information that a fab generates. Game-changing, more holistic applications become possible when this information can be combined in new and informative ways. As it turns out, a data source is just a data source, but users in different factory areas need to extract different information from these common data sources. They need different applications and portals – in other words “views” – that are adapted and adjusted for each area’s needs. Aren’t we smart enough? Some people think that 300mm fabs are already smart. That’s true. They are. But, they could be a lot smarter. No 300mm fab in use today has attained the full, utopian vision of what a smart factory can deliver over the next 10 years. When you finally integrate all of the disparate databases in a fab – when you’re able to use all of those different data sources as one common data source – that’s when your Smart Factory will have the ability to self-optimize its future actions and react quickly to real-time events. The largest semiconductor manufacturers tend to develop these smart factory applications on their own. The remaining semiconductor fabs need to work together with other fabs and their solution providers to develop these smart factory applications. Why now? Why is everyone talking about “Smart” now? It’s because the semiconductor industry has helped to create all of the enabling technology: the compute power, the networking and networking standards, and even the industry’s maturation into a multi-tiered organization of solution providers. We’ve reached the point where we can collect data from a widespread sensor network along with tool-health data and we can then warehouse this data so that it can be applied to more intelligent decision-making. While there may be one or two sensors on a tool today, in the future there will be many such sensors connected over an IoT network or networks that provide mountains of data to the warehouse. All of this data will feed into the digital-twin version of the fab. One of the biggest changes on the horizon made possible by all of this accessible data is advanced scheduling. Despite all of the automation advancements made over the past 25 years, including robotic handling, it’s still hard to decide “where, what, and when?” for every single lot in the factory. Today, no factory in the world is more complex than a semiconductor fab. Optimizing a semiconductor manufacturing process is the most complex manufacturing-optimization task in the world. Do it for ROI ROI is the chief reason for having a digital twin. Once you can make a truly smart, holistic schedule of the fabs operations — not a dispatch or rule-based dispatch list — then you can create an operationally smart factory. Rule-based dispatching systems primarily focus on tools and tool-centric views. Although they incorporate knowledge from current WIP and tool conditions to make decisions better than simple dispatch systems, smart factories are not just about tools and the current WIP at them. Smart factories use the status of every tool and lot in the factory to make fab-centric optimizations instead of tool-specific optimizations. Once you have a digital twin, you’re optimizing for global functions such as line linearity and on-time delivery. These functions are not just about the moment. The transition to a smart factory thus represents a huge philosophical change. When you know exactly what’s going to happen in a factory over the next 12 hours for every single lot, every single wafer carrier, and every single entrance port of every tool in the factory, then you suddenly have control over the factory’s idle time. You know when you can optimally perform PM (preventive maintenance). You know how to best redirect material or labor resources to maximize output. You can create a smart schedule for every maintenance person in the factory that comprehends each person’s skill set and tool downtime so that there’s no negative impact on the factory’s productivity. You can only do all of this when you know the future. Figure 2 illustrates the opportunity. Imagine that a factory contains 1,500 tools. Use of these tools is scheduled for the next twelve hours. The information depicted in Figure 2 encompasses process changes from one chemistry to another, implant changes, reticle changes, and the status of every single consumable for all 1,500 tools. The white spaces that appear between processes in Figure 2 represent opportunities to intelligently schedule events such as maintenance to maximize factory productivity. Figure 2: Smart scheduling permits factory-wide optimization to maximize productivity. Once you have a schedule, you need to translate that schedule into actions or movement. It’s not easy to do this and most material-control systems today make overly simplistic decisions based on modeled assumptions and typical cases rather than the actual time each lot needs to be at a precise location, which can only come from a schedule. Once the data from all of the tools is connected, a smart scheduling system can use the digital twin to make far better process decisions. The larger the factory (or more complex the factory), the more important it is to make smarter decisions. Note: SEMI has a Smart Manufacturing Technology Community. For more information or to get involved, click here. If you would like to discuss Smart Manufacturing more with John directly, he can be contacted at [email protected]. John Behnke is general manager of the Final Phase Systems product line at INFICON.

Artificial intelligence (AI) is on the verge of transforming entire industries as it gears up to power semiconductor industry innovation and growth, thrusting the technology to front and center at SEMICON Japan 2019, December 12-14 at the Tokyo Big Sight (Tokyo International Exhibition Center).The SMART Technology Forum at SEMICON Japan will highlight the latest AI developments and trends. Supported by U.S. Commercial Service in Japan, the forum will feature Yutaka Matsuo of the University of Tokyo. An authority on AI, Matuso will give an overview of both AI business and technology. His presentation will be followed by an AI outlook from Microsoft Japan, Amazon Web Services and DefinedCrowd.A number of Japanese startups are on leading edge of AI innovation in machine and deep learning. One is Preferred Networks Inc., a company that applies cutting-edge deep learning technology to Internet of Things (IoT) applications across transportation, manufacturing and healthcare.In his opening day keynote at SEMICON Japan, Toru Nishikawa, president and CEO of Preferred Networks, Inc., will highlight the latest developments and promise of using deep learning for industrial applications. Nishikawa will unpack how AI companies jockeying for competitive advantage will win by harnessing technologies to process massive amounts of data efficiently and quickly.Following is look at Preferred Networks, Inc. and five other Japanese startups that are driving AI innovation. Within Japan's world of AI, machine learning, and deep dearning, Preferred Networks is likely the most well-known Japanese company. The parent company, Preferred Infrastructure, was founded in March 2006 by Toru Nishikawa and Daisuke Okanohara, who focused on search engine development before turning to machine learning and establishing Preferred Networks to commercialize the technology.Preferred Networks established itself as one of the world’s top providers of machine learning technology with the development of Chainer – an open source deep learning framework that has been offered free of charge since June 2015 and was released before TensorFlow, Google’s renowned Deep Learning framework. Established in 2012, ABEJA is thought to be Japan’s first venture company to specialize in deep learning. ABEJA's core technology is its AI platform ABEJA Platform. Based on this platform, the company offers various solutions to more than 100 client companies. ABEJA also offers ABEJA Insight, a specialized package service for the retail and distribution, manufacturing, and infrastructure industries. Data analytics provider BrainPad Inc. was the first Japanese AI venture listed on the Tokyo Stock Exchange. Established in 2004, before the advent of big data, BrainPad Inc. cultivated a vision of analyzing vast amounts of data in increase the competitiveness of Japanese companies. LeapMind Inc. aims to offer deep learning technology that uses fewer computing resources and draws less power. Both are important capabilities since deep learning requires considerable computing resources to perform image and speech recognition. The company’s answer to this deep learning challenge is a small form factor FPGA with low power consumption.In April 2018, LeapMind started offering the tool DeLTA-Lite to support model construction for Deep Learning. The tool simplifies the development of deep learning design models, eliminating the need for model design, hardware, and software expertise. Hacarus Inc.’s HACARUS-X AI technology, which combines sparse modeling and machine learning technology, features low power consumption and small devices such as FPGAs. In collaboration with semiconductor trading company PALTEK, Hacarus is integrating HACARUS-X algorithms with Xilinx's FPGA Zynq UltraScale + MPSoC. Both companies area also implementing HACARUS-X algorithms in a box computer.Sparse modeling is gaining attention as a modeling method by which humans can understand the judgment process of AI by extracting features from a small amount of learning data. With expertise in life science fields such as medical and biology and image processing technology, LPixel, Inc. develops image analysis systems with original algorithms and machine learning techniques. It has developed a cloud-based AI image analysis platform and an AI medical image diagnosis support technology that streamlines the review of large amounts of research data and detects image fraud in research papers and other documents for the medical and biology fields, freeing researchers to devote more time to their core work. Yoichiro Ando is a marketing director at SEMI Japan.

Nicolas Sauvage, senior director of Ecosystem at TDK InvenSense, will present at the fast-approaching MEMS Sensors Executive Congress on October 29-30, 2018 in Napa, Calif. SEMI’s Nishita Rao spoke with Sauvage to offer MSEC attendees advance insights on Sauvage’s feature presentation.SEMI: What is “autonomy value” and why is it important?Sauvage: How do you increase the perceived value of an electronic device? If it’s an autonomous car, its value is closely tied to the autonomy level — i.e., the independence — that it offers people. Higher autonomy value for a self-driving car, for example, means that even a blind person could use it. It’s been almost two years since Waymo demonstrated this, and here’s the video that shows it.Countless other sensor-based electronic products have their own “autonomy value.” Imagine the need to get medicine to people during a humanitarian health crisis. Drones could be your best option because they can deliver to inaccessible or remote locations. Unlike older drones, which require active piloting by a person, a drone with higher autonomy value could deliver medicine to Doctors Without Borders without ongoing human intervention.This drone could navigate objects, such as trees and birds, and would have excellent location-awareness. It could fly through any landscape in bright sunlight or during the night. To increase the drone’s autonomy value, you would need better sensors, including those sensors that can enable sensing in sunny conditions or in pitch-black night, as well as better machine learning.SEMI: In this example, what types of sensors would the drone manufacturer need?Sauvage: The manufacturer would need a “surrounding-sensing” solution that includes ultrasonic and pressure sensors as well as image sensors. Start with high-quality image sensors combined with ultrasonic range-finding sensors — high-accuracy devices that function in all lighting conditions and can detect objects of any color. Add motion sensors and a pressure sensor, which would capture the height of the drone to make known the drone’s location in space. The drone would need this combination of sensors, plus smart sensor fusion, because GPS alone cannot avoid obstacles: its signal can be sporadic in certain parts of the world or in certain terrain, making it unreliable.A key attribute of all these sensors would be low power consumption since the drone would run on battery.SEMI: To what extent might autonomy value cause manufacturers to consider multi-vendor solutions?Sauvage: I would like to see it inspire the MEMS and sensors ecosystem to work together, to arrive at multi-vendor solutions that will benefit humanity through greater autonomy value. Whether we’re looking at autonomous cars, drones, robotics or other applications, there are cases where we need to prioritize safety and security over industry competition. SEMI: Where are we today in terms of achieving true autonomy value – and where are we going?Sauvage: The sky is the limit, literally. Machine learning and surrounding-sensing solutions applied to cars, drones and robots will increase autonomy value to the point where we can justifiably call it artificial intelligence.SEMI: What would you like MEMS Sensors Executive Congress attendees to take away from your presentation?Sauvage: I hope that attendees will recognize the value of ecosystem solutions in increasing autonomy value. Together we can expand the variety of sensor types that address novel use-cases and jobs-to-be-done. Instead of waiting for customers to ask for ecosystem-level solutions, we need to articulate a complete MEMS and sensors supply-chain ecosystem if we want the Internet of Things (IoT) and Industrial IoT (IIoT) to grow more quickly. As senior director of Ecosystem, Nicholas Sauvage is responsible for all strategic relationships, including Google and Qualcomm, and other HW/SW/System companies. He is also responsible for strategic and market-driven goal-setting of our SensorStudio developer program, and driving select partnerships with SoC sensor hub platforms. Prior to joining InvenSense, Nicolas was part of NXP Software management team, responsible for worldwide sales, as well as for P L and product management of their OEM Business Line. Nicolas is an alumnus of Institut supérieur d’électronique et du numérique, London Business School and INSEAD. Register today to connect with Nicolas Sauvage at the event. You can also connect with him on LinkedIn.Nishita Rao is a marketing manager at SEMI.