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Tracking and localization technologies typically integrate with Wi-Fi and Bluetooth signals to pinpoint the location of people and objects. But what if a venue can’t install beacons or routers, or afford to deploy Wi-Fi or Bluetooth networks? Thanks to a combination of proprietary algorithms, advanced sensor fusion and the natural geomagnetic field, GipStech, a spin-off of Università della Calabria, built an indoor localization and navigation technology platform for accurate localization in the absence of an adequate GPS signal.Ahead of the SEMI MEMS Imaging Sensors Summit, 25 to 27 September 2019 in Grenoble France, Serena Brischetto of SEMI spoke with Gaetano D'Aquila, co-founder and CEO of GiPStech, about sensor fusion, augmented GPS applications and the future of indoor localization. Join us in Grenoble to learn more about GiPStech and meet other MEMS, imaging and sensors experts. Registration is open online.SEMI: Early this year GiPStech completed a test deployment of the first high-precision, infrastructure-free navigation system at Tokyo Shinjuku metro station in Japan. This is the busiest transportation hub globally! What were the main challenges you faced and how did your technology enable such a highly complex indoor localization?D'Aquila: As you mentioned, Shinjuku station in Tokyo has been registered in Guinness World Records as the busiest transportation hub globally. With 36 platforms, 200 exits and countless corridors and connections, it is easy to get lost there, especially for foreigners and tourists. On the other hand, this scale and complexity makes it unfeasible and expensive to install Bluetooth or similar infrastructure for standard indoor localization.For this reason, we needed to provide a cost-effective indoor localization technology without installing any kind of artificial supporting infrastructure. Thanks to our GiPStech patented multi-sensor-fusion localization stack and the high density of public Wi-FI networks, it’s possible to determine when passengers are inside the station. The public Wi-Fi networks signals were fused as an additional source in GiPStech's sensor-fusion platform to complement the inertial and geomagnetic engine and deliver very accurate results across the entire station. The tests performed in the station also demonstrated that the localization system can even detect the floors where travelers are walking. Now we are ready to roll out the same setup in other stations and environments.SEMI: You are not the first to pursue infrastructure-free indoor localization, but your technology platform seems to be very accurate in bringing precision, stability and consistency to the user experience. What lead to those advancements and incredible results?D'Aquila: Our key differentiating factors are built in the approach we created after years of research and development. One differentiation, of course, is related to our expertise and know-how about how the geomagnetic field can be used as a driving signal for the localization process.During R D we constructed and patented a modular multi-sensor-fusion software stack to solve any kind of localization problem, mainly in indoor environments. We started from a single-signal approach based on the employment of the geomagnetic field as a localization signal. But, mainly due to the very inaccurate devices chosen to measure the geomagnetic field, such as the smartphones that everyone carries in their pockets, we noticed that this single-signal approach is accurate but not reliable because it is strongly affected by a key weakness – the quality of sensor in the device.SEMI: How long did it take for you to solve this issue?D'Aquila: We started to integrate other signals within a few months after the first field tests related to the employment of the geomagnetic field alone. We also began to develop a software platform that could fuse any signal source (natural or artificial) available in the environment to preserve the reliability and accuracy of the localization system when some of these signals are temporarily affected by poor measurement quality. This is our differentiating factor today. We can re-configure our software platform to provide the best reliability and accuracy with the lowest artificial infrastructure in almost any context – from outdoor in a seamless way to indoor and vice versa.SEMI: GiPStech’s inertial engine is one of your cutting-edge technologies that completes your advanced indoor navigation and localization software stack. How do you see the technology evolving?D'Aquila: The inertial engine was one of our first technology modules mainly developed to enhance reliability, smooth the signals and reduce the computational power requirement of our geomagnetic localization approach.After a while, together with a third party that evaluated the performances of our module, we noticed that this module not only can be used as a self-standing localization technique, but it can also deliver high accuracy mainly in PDR (pedestrian dead reckoning) applications.Today our PDR is itself a black box with embedded subsystems. Besides some filtering modules, it includes a state-of-the-art step detector that detect steps even when the person changes the smartphone position and location (not only in the hands but also in backpacks or pockets) and an advanced step validation module that identifies and rejects fake steps.If you’ve ever used a commercial fitness tracker attached to your wrist, you know that in most cases if you move your arm the device will counts some steps that, of course, are not real. Our step validator solves this problem by detecting only real steps – a very important capability that allows our PDR to be employed as a self-standing inertial navigation system. We developed the PDR with strong attention to maintaining low requirements for the computational power and memory footprint. These additional characteristics makes the PDR very interesting even for a direct integration of the software at the silicon level in modern MEMS sensors.In a nutshell, the ability of MEMS sensors to run directly an embedded software module will drive technology enhancements that will allow some of the functionalities now available through an external application processor, such as those in smartphones, to move to a lower level (in the silicon). This, of course, reduces power consumption while even increasing the number of value-added services, including localization services, that could be built directly on top of the MEMS without requiring external software and/or application processor.SEMI: Do you think indoor localization will be more applicable in the next 10 years in areas such as Smart manufacturing, travel, healthcare, entertainment and retail?D'Aquila: Several market reports and our business development experience lead us to assess which sectors are of greatest interest for the application of indoor positioning technologies. They include the following. Industry (manufacturing logistics) Healthcare (tracking of assets, patients and doctors) Big installations (visit experience for museums, fairs) Airports stations (both for travelers and for resource and operation management) Large distribution (user profiling and influencing of the purchasing behavior) Indoor localization is a key enabling technology. Adoption, mainly in these sectors, was limited by the unfavorable tradeoff between cost and benefits. Our indoor localization technology aims to overcome those tradeoffs to make its adoption much more cost-effective while providing the best possible reliability and accuracy.SEMI: What are your expectations regarding the summit in Grenoble, and for the future of the sensors technology ahead? Where are we heading?D'Aquila: Many sectors would benefit from indoor localization technologies. MEMS, imaging and sensors are driving innovation and explosive demand for transportation, medical, mobile, industrial and other IoT applications. But these devices also constitute the basic building blocks for the development of reliable and affordable localization technologies.In outdoor environments we are pretty covered by the GPS. Indoors, where we spend more than of 80 percent of our time, similar types of services are coming to the market now and becoming more reliable over time.This Summit facilitates the direct interaction between different stakeholders to act at different points in the MEMS sensors value chain. Indoor localization was an emerging technology unrelated to the sensors ecosystem until now. Today, indoor localization must leverage MEMS sensors to be effective and reliable. In the future, localization technologies will be embedded directly in silicon to deliver the best performance at a lower cost to increase their adoption for more applications.Gaetano D'Aquila served as research fellow from 2002 to 2004 at the CNR and as an assistant teacher at the University of Calabria. From 2003 to 2014, he worked in the industry first as a security consultant for Telcos and Banking in Value Team S.p.A. and then as project manager at Infomobility S.p.A., where he coordinated research and development and strategic activities in the automotive and auto insurance industries. In 2014 he co-founded GiPStech and is its current CEO. He has published several papers in scientific journals and has filed for seven patents, three of which have been granted in the U.S. and Europe. Gaetano has a MSc in Computer Engineering and a Ph.D. in Science and Engineering of the Environment, Buildings and Energy from the University of Calabria, Italy.Serena Brischetto is a marketing and communications manager at SEMI Europe.

Editor's note: Arm and Samsung Foundry are extending their collaboration on FD-SOI, which they'll be highlighting at the SOI Consortium's Silicon Valley Symposium April 9th. In the meantime, Arm Senior Product Marketing Manager Umang Doshi described the range of projects in a recent Arm Community / Developer physical IP blog. We thank Arm for sharing this blog with ASN readers.~ ~~ Samsung Foundry and Arm FDSOI collaboration announced By Umang Doshi The challenge with designing at newer and more advanced process nodes is that things generally don’t get less complex and expensive, much as we might want this. Still, the upside to each new process node, generally, is that you can build more highly efficient and targeted devices to address more markets and applications in a timely fashion. For the complexity and cost challenges, however, there’s good news: Arm and Samsung Foundry just announced a comprehensive, foundry-sponsored physical IP platform, including an eMRAM compiler at 18FDS (18nm FDSOI). In addition, the Arm offerings for 18FDS include three POP IP packages for Arm Cortex-A55, Cortex-R52 and Cortex-M33 processor IP. The platform will help drive new leading-edge designs in power-sensitive applications in 5G, artificial intelligence (AI), automotive, Internet of Things (IoT), and other market segments. It’s the industry’s first, fully comprehensive physical IP platform that includes an eMRAM compiler at 18FDS. 28nm: Before the breakthrough One of the most widely embraced nodes, 28nm the so-called “forever node,” has done wonders for industry innovation over the years. However, leakage power is still challenging for planar transistors. Engineers deployed high-K metal gate (HKMG) at 28nm, to combat leakage, but it’s still an issue. That’s because the channel underneath the gate is too deep and too far from the gate to be well-controlled, which results in higher leakage power. Solutions for the leakage issue have prompted designers to embrace FinFETs and FDSOI (fully-depleted silicon-on-insulator) with thinner channels that enable greater control by the gate. Indeed, FDSOI is gaining traction in the market place. By construction, 28nm FDSOI enables much better transistor electrostatic characteristics versus conventional bulk technology. 28nm FDSOI offers: Wide Forward/Reverse Body-bias range and flexible Poly bias (PB) range to tradeoff power/performance. Better performance and power than bulk process technology. Better resistance to radiation and SER. Less sensitive to variability because there’s no channel doping. Ultra-low power voltage (operating at low voltages in the hundreds of millivolts range). Easy migration from bulk versus the previous SOI version, PDSOI (partially-depleted silicon-on-insulator), required unique timing and power models. What’s more, there are cost benefits today and more forecast for the future. Arm and Samsung Foundry extend FDSOI leadership from 28FDS to 18FDS In 2018, Arm announced the industry’s first Embedded MRAM (eMRAM) compiler IP built on Samsung Foundry’s 28FDS process technology. Since the announcement, Arm has engaged with several Samsung Advanced Foundry Ecosystem (SAFETM) partners on a landscape-changing collaboration to deliver the industry’s first 28FDS eMRAM-enabled IoT silicon system demonstrator telling the Arm IoT story on Samsung Foundry silicon. Coupled with Arm’s IoT ecosystem, Pelion IoT Platform and Platform Security Architecture (PSA) solutions, this 28FDS eMRAM-enabled IoT demonstrator will showcase a new-generation of secure and energy-efficient IoT edge devices which integrates software stacks offering secure boot, firmware updates, on-chip storage, chip to cloud communication and device/software provisioning. The combination of 28FDS and eMRAM non-volatile memory brings new opportunities for a new class of highly integrated and energy-efficient designs. We’re thrilled that Samsung Foundry has extended its successful collaboration on FDSOI technology from 28FDS process to 18FDS. With the new platform, 18FDS is a cost reduction solution with lower power and same back end of line (BEOL) as 14nm FinFET. It has RF and eMRAM support to enable the widest range of different applications. “18FDS is the next-generation node on Samsung's FD-SOI roadmap with enhanced power, performance, and area (PPA)," said Jaehong Park, executive vice president of Design Platform Development at Samsung Electronics. “The relationship between Samsung Foundry and Arm stretches back more than a decade and has helped put the right design technology in the hands of the world’s leading designers. The enhanced PPA from our 18FDS process combined with Arm cores and Artisan Physical IP will again bring the cost and time-to-market advantages to enable the competitive and differentiated SoC designs.” Highlights on Arm-Samsung 18FDS platform Includes seven memory compilers, three logic libraries, two (1.8 and 3.3V) GPIO libraries, three POP IPs and the eMRAM memory compiler. Supports automotive AEC-Q100 Grade 1 design requirements, and comes with ASIL-D support and a complete automotive safety package. Utilizes back biasing supported by the FDSOI technology to help achieve low leakage by using reverse body-bias technique or a performance boost using forward body-biasing. This is a key differentiation of 18FDS platform. Supports Logic Corner Generator (LCG) and Memory Compiler Corner Generator (MCCG). LCG and MCCG products allow designers to generate custom corners with body-bias voltages to take the maximum advantage of body biasing power-performance flexibility. 18FDS will help enable the development of new devices connecting consumers in entirely new ways, whether it’s in AI, 5G mobile, automotive or other areas. The platform will be available in late 2019. Arm's Physical Design Group has a track record of successful implementations with Samsung Foundry across multiple generations of process nodes and products. Besides 28/18FDS, Samsung Foundry and Arm also have 14LPP/LPC, 11LPP, 7LPP and 5LPE platform collaborations. Interested in knowing more about Artisan Physical IP at 28/18FDS? Come join us at SOI Silicon Valley Symposium on April 9 at Double Tree by Hilton, San Jose, California. During the event, you will have the opportunity to hear how Arm and other industry leaders work together to accelerate the adoption of FDSOI technologies including products and applications. Alternatively, you can also reach out to us with your inquiry.

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.

Jason Jelinek, a software technical manager at John Deere Electronics Solutions, has parlayed his more than two decades of embedded software engineering experience into commercializing controls and sensing technologies for rugged/harsh environments, including agriculture/off-road and aerospace. During his keynote at the upcoming FLEX and MEMS Sensors Technical Congress 2019, February 18-21 in Monterey, Calif., Jelinek will address the driving need for advanced sensing technologies that will fuel the continued growth of autonomy in agriculture.SEMI’s Maria Vetrano asked Jelinek to help FLEX/MSTC attendees understand his vision of autonomy in agriculture, which heavily leverages advanced sensing technologies to help farmers master equipment logistics, handle vehicle- and fleet-level operational efficiency, and manage the entire lifecycle of crops.SEMI: Did autonomy in agriculture start with autonomous equipment, such as tractors and combines?JELINEK: Automation, the first step on the road to autonomy, has been occurring in agriculture for a long time. Over the past 100 years, automation has dramatically reduced manual effort and simplified jobs in farming, allowing operators to focus more on administrative and other aspects of their work.The evolution of the combine is a good example of automation in agriculture. Long ago farmers would use a scythe to cut down the crop before bundling or stacking it up. Later they would manually thresh and winnow the crop to get the grain. Over time, we developed windrowers to cut the grain, threshing machines to separate the grain from the chaff, and winnowing machines to get only the grain. Combines now “combine” all those steps to go from grain on the stalk in the field to grain in the hopper. One person in a combine can do the work that once required many people and animals — all in a much shorter timeframe. We are now looking at automating harvesting to maximize yield and reduce fuel consumption. The AUTOTRAC feature on John Deere machines is a recent example. AUTOTRAC divides a field into rows based upon the parameters of the machine in operation, supporting hands-free driving with very high accuracy. It allows consistent, accurate rows for tilling, planting, crop treatment and harvesting, saving considerable time, improving overall quality and freeing the operator to do other work while in the vehicle.The Exact Emerge and Section Control features (which also use AUTOTRAC) will spur greater future autonomy. Control over both the seed spacing (Exact Emerge) and when the machine drops seeds (Section Control) prevents overseeding and provides the right seed-spacing for optimal crop production.As we look to the future, sustained growth in automation of jobs will enable the development of fully autonomous equipment. Currently, however, skilled operators are still closely involved in job management and execution. To realize greater autonomy, we will need machines that make the decisions once made by people.SEMI: How will autonomy in agriculture change the ways that we grow and harvest food — and even affect when we sell it?JELINEK: Autonomy will lead to more efficient production, reducing fuel, fertilizer, herbicide and water requirements. It will also enable fewer people to do more of the work.Let’s start with conditions that are hard, even impossible, to control: weather and staffing.While farming is still tied to the weather — and will remain so for some time — more efficient operations will allow tilling, planting, spraying and harvesting of fields to occur in shorter time windows that more easily match conducive weather conditions.There is also a human-resource issue: The agricultural industry must compensate for population decline in the rural areas where farmers operate. Doing more with less is essential for agriculture to continue to meet the rising food and clothing demands of the world’s population.SEMI: To what degree will we see artificial intelligence in autonomous agricultural systems?JELINEK: While autonomous systems had their start at the vehicle level, they will one day move to the entire fleet, providing suggestions on when the owner should execute operations. Autonomous systems may also help owners to decide when to store or sell crops, based on market conditions, operating costs and desired margin levels. That’s the initial level of artificial intelligence that I foresee.SEMI: How can sensing improve autonomy in agriculture?JELINEK: The challenges we face in agriculture are many, but technology will help us meet them. We must transfer responsibility for operations and decision-making from the skilled operator to the intelligent machine. Through increased use of sensing, we can gather large amounts of data, which autonomous agricultural systems will process, communicate and interpret to streamline jobs and boost agricultural production.SEMI: What would you like FLEX/MSTC attendees to take away from your presentation?JELINEK: I would like FLEX/MSTC attendees to understand the environment in which agricultural sensors need to operate. We need sensing solutions that will survive and thrive in rugged, outdoor variable environments to support the automation that will fuel autonomy.I would also like to engage suppliers in the application of current technology to meet our sensing needs.Jason Jelinek will present Autonomy in Agriculture at FLEX/MSTC on Tuesday, February 19 at 9:00 am.Register today to connect with him at the event. To learn more, click here.MSTC Flex 2019 is organized by the MEMS Sensors Industry Group (MSIG) and FlexTech. Maria Vetrano is a public relations consultant 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.

New SEMI Taiwan Testing Committee to strengthen the last line of defense to ensure the reliability of advanced semiconductor applications.Mobile, high-performance computing (HPC), automotive, and IoT – the four future growth drivers of semiconductor industry, plus the additional boost from artificial intelligence (AI) and 5G – will spur exponential demand for multi-function and high-performance chips. Today, a 3D IC semiconductor structure is beginning to integrate multiple chips to extend functionality and performance, making heterogeneous integration an irreversible trend. As the number of chips integrated in a single package increases, the structural complexity also rises. Not only will this make identifying chip defects harder, but the compatibility and interconnection between components will also introduce uncertainties that can undermine the reliability of the final ICs. Add to these challenges the need for tight cost control and a faster time to market, and it’s clear that semiconductor testing requires disruptive, innovative change. Traditional final-product testing focusing on finished components is now giving way to wafer- and system-level testing.In addition, the traditional notion of design for testing, an approach that enhances testing controllability and observability, is now coupled with the imperative to test for design, which emphasizes drawing analytics insights from collected test data to help reduce design errors and shorten development cycles. Going forward, the relationship among design, manufacturing, packaging, and testing will no longer be un-directional. Instead, it will be a cycle of continuous improvement.This paradigm shift in semiconductor testing, however, will also create a need for new industry standards and regulations, elevate visibility and security levels for shared data, require the optimization of testing time and costs, and lead to a shortage of testing professionals. Solving all these issues will require a joint effort by the industry and academia. "With leading technologies and $4.7 billion in market value, Taiwan still holds the top spot in global semiconductor testing market," said Terry Tsao, President of SEMI Taiwan. "When testing extends beyond the manufacturing process, it can play a critical role in ensuring quality throughout the entire life cycle from design and manufacturing to system integration while maintaining effective controls on development costs and schedules. Taiwan's semiconductor industry is in dire need of a common testing platform to enable the cross-disciplinary collaboration necessary for technical breakthroughs."The SEMI Taiwan Testing Committee was formed to meet that need, gathering testing experts and academics from MediaTek, Intel, NXP Semiconductors, TSMC, UMC, ASE Technology, SPIL, KYEC, Teradyne, Advantest, FormFactor, MJC, Synopsys, Cadence, Mentor, and National Tsing Hua University to collaborate in building a complete testing ecosystem. The committee addresses common technical challenges faced by the industry and cultivates next-generation testing professionals to enable Taiwan to maintain its global leadership in semiconductor testing.The SEMI Taiwan Testing Platform spans communities, expositions, programs, events, networking, business matching, advocacy, and market and technology insights. For more information about the SEMI Taiwan Testing platform, please contact Elaine Lee ([email protected]) or Ana Li ([email protected]). Emmy Yi is a marketing specialist at SEMI Taiwan.

4 Key Takeaways from SEMI Taiwan Member ForumThe rapid development of artificial intelligence (AI) has accelerated the digital transformation in various industries and has now fused with Internet of Things (IoT) to exploit the value of both technologies in reshaping the electronics industry value chain. As it emerges from the shadows of its parent technologies, AIoT is giving rise to new opportunities in manufacturing, healthcare, transportation, and even energy. AIoT is fast rising in prominence as an enabler of key electronics manufacturing process improvements and the creation of add-on value to existing products – both critical to the success of many businesses.SEMI and the SEMI MEMS Sensors Industry Group (SEMI-MSIG) held a technical forum on smart sensing and its applications in AI and AIoT, inviting renowned experts in sensors and edge computing to share in-depth insights into the latest AIoT technologies and applications with more than 100 industry professionals in research and development, marketing and sales. Here are four key takeaways from the SEMI Taiwan member forum.1. Steady Growth for Global Sensors MarketThe global sensors market’s steady growth is expected to expand at a CAGR of 6.6 percent from 2017 to 2023, with Asia driving the biggest gains and automotive leading the segments – including healthcare and education – with the strongest growth. Automotive alone is expected to reach US$34 billion in 2023.2. Integration Critical to MEMS Sensors DesignsWith AI booming, MEMS sensor designs need to drive toward greater integration —not only integrating data collection with sensors, but also streamlining data processing on the backend – making 3D models of today’s MEMS mechanical designs critical. The differences between 3D and entrenched 2D models are dramatic, elevating the importance of specifying manufacturing steps in MEMS designs. As new sensors and applications continue to emerge, companies that develop the most powerful integrated designs will win. 3. Growth of Smart Voice-Control Applications to ExplodeAIoT is also accelerating the development of smart voice-control applications and the rise of new related business opportunities. Just 50 million voice-controlled devices shipped worldwide in 2017, a number predicted to swell to 436 million in 2021 with smart home devices such as set-top boxes and smart TVs the major growth drivers.4. AIoT Eyed to Make Human-Robot Collaboration SafeSafety is an essential feature for human-robot collaboration. Tactile sensing technologies give robots a layer of “skin” with capabilities rivaling human touch. To ensure humans and robots work together safely in work environments, sensors on this layer of skin are concentrated – less than 8mm apart, equivalent to the width of a human finger, with a response time of less than 5ms on contact. More than 4 million robots worldwide are expected to be upgraded with these sensing technologies and are on track for deployment in pilot plants in the next three years.SEMI-MSIG is committed to strengthening connections across all sectors in the MEMS and sensors supply chain, working closely with the industry to accelerate the development of related technologies and applications in both mature and emerging markets. In addition, SEMI-MSIG hosts regular events to inspire business opportunities and technology exchange for innovative applications, while enhancing the visibility of members among global customers and partners to help them forge new partnerships. To join the group, contact SEMI Taiwan’s Helen Chen at [email protected] Yi is a marketing specialist at SEMI Taiwan.