IoT

SEMI has guided a proof-of-concept (POC) project since 2018 at Cornell University. Funded by the Army Research Labs to demonstrate an AI model that predicts the precise patterned features on the chip – the tinier and more precise the features, the higher the chip performance.

More than 4,000 participants convened in person at the four-day SEMICON Europa Expo in Munich, November 16-19. Attendees gathered at the Executive Forum for insights into the latest trends in the semiconductor industry and heartily welcomed the long-awaited return to in-person networking.

SEMICON Europa, SEMICON West, SEMICON Japan, & SEMICON Taiwan welcome back exhibitors and visitors to show floors starting in mid-November as four of SEMI’s seven flagship events gather visionaries and industry leaders to explore the latest semiconductor trends and innovations by the end of 2021.

Dr. Mousumi Bhat, senior director of External Manufacturing at Micron Technology and first female member of the SEMI Southeast Asia Regional Advisory Board, discusses her passion for sustainable business practices and her work driving the transformation of Micron’s manufacturing plant in Singapore.

While Artificial Intelligence (AI) emerged in the 1950s, only in recent years have AI applications proliferated with the explosion of data and continuing improvements in Moore’s law that have driven rising processing speeds. Voice assistants, image analysis software, search engines, and speech and facial recognition systems were among the first applications to use AI. Today, adoption has spread to sectors such as agriculture, cybersecurity, healthcare, software development, e-government and the intelligent enterprise to generate jobs and help spur economic growth. The Edge AI Opportunity and the Microelectronics IndustryAI can be embedded in hardware devices such as advanced robots, autonomous cars, drones or Internet of Things (IoT) applications. Today, according to the EU’s digital strategy, data centres and other centralized computing facilities account for the vast majority – 80% – of AI data processing and analysis, with smart connected objects such as automobiles, home appliances and manufacturing robots that bring the compute function closer to the user representing 20%. The latter, known as Edge AI applications, are powered by edge-based machine learning chipsets, not the AI chipsets designed to run cloud-based machine learning algorithms.The EU’s white paper on AI published in February 2020 anticipates that the way data are stored and processed for AI applications will change significantly over the coming five years as edge computing applications proliferate. Most AI applications need to connect with devices that collect data and manage data flows. When the applications connect with cloud infrastructures to train large volumes of data for a machine learning model, the interface devices often require hardware support. Edge AI can minimize data transport by processing data directly from local devices to accelerate data analysis and decision-making and make data transport or accelerator hardware unnecessary, critical in reducing power consumption and enhancing data security for applications such as autonomous driving. Over the past 40 years, the ICT sector has been continuously increasing greenhouse gas (GHG) emissions despite efforts to shift to renewable energy. Cloud-based AI applications require an ICT infrastructure for high-performance computing and high-speed connectivity. According to MIT Technology Review, data centres’ AI workloads could account for a tenth of the world’s electricity usage by 2025. a mass update of cloud-based AI applications may significantly increase energy consumption, unlike with Edge AI. This is why the strategy for developing Edge AI is well-aligned with the EU’s Green Deal objectives. Europe aspires to play a leadership role in Edge AI to strengthen the sector’s competitiveness and protect the European digital sovereignty. Europe’s strong industrial competencies in embedded systems and microcontrollers will help the region promote development of European domestic AI solutions for emerging high-value IoT applications in industrial processes such as Industry 4.0, Connected and Automated driving (CSA), smart cities, climate action, healthcare, and national defence and security. With this strong strategic position in technology, Europe is well-positioned to invest to become the leader in the Edge AI global market.Preparing the Workforce for the Microelectronics IndustryTo design and manufacture leading Edge AI chipsets, European education providers and industry will need to work closely together to train the current and future workforces. Within the framework of the METIS project, a four-year project co-funded by the European Commission through the Erasmus+ programme, SEMI and imec deployed experts in the field to survey and interview focus groups. The survey identified the following key focus areas for workforce development: 1. True Capability of AI and Data Science With AI’s heavy dependence on data, the workforce of the future must be trained in areas of data science including data integrity to ensure quality, unbiased sourcing, collection and accurate analysis necessary to interpret huge volumes of data. Europe also needs to train the next generation of AI chip designers in data security and privacy – key challenges to the widespread deployment of Edge AI chips. 2. Climate Change, Sustainable Development Goals (SDGs) and Social Inclusion TrainingSince the industry must be able to develop Edge AI solutions to enable the digital transformation while limiting GHG emissions, microelectronics engineers need to be schooled in climate change and understand how their work contributes to meeting the United Nation’s Sustainable Development Goals (SDGs). Workplace diversity and social inclusion are also important target areas for education since Edge AI applications should serve various groups of people with different needs.3. EthicsChip industry workers must also be educated in ethical issues of AI related to the technology’s potential societal impact in the near future[1]. With AI applications capable of monitoring Internet searches based on users’ personal preferences and biases to deliver tailored advertising, news and other information, developers must recognize how the technology can influence thinking and behaviour of individuals and groups. This awareness can help developers strike a balance between supporting commercial interests and societal good so the microelectronics industry can ensure ethical implementation of AI. 4. Cross-disciplinary Skills Required for AIAI development requires a comprehensive, cross-disciplinary skill-set to be able to integrate the work of specialists from diverse educational, cultural and professional backgrounds critical to developing non-biased AI solutions. For example, in addition to technical expertise, microelectronics AI developers must be able to communicate clearly and work in close-knit teams with non-technical experts from business, law, medicine and the social sciences.What’s Next?The microelectronics industry has a tremendous opportunity to develop new chip-based solutions for AI architectures, and apply AI techniques to improve operational efficiencies of design and manufacturing. To seize this opportunity, the industry must work closely with education providers to groom the next generation of skilled workers. This tight collaboration is critical to designing and delivering specialised courses to college and university students as well as engineers now working in the chip sector. The stakes are high. By preparing workers to develop Edge AI chipsets, the microelectronics industry can help the world confront some of the greatest challenges it faces today.For more information, see SEMI Responds to European Commission White Paper on Artificial Intelligence.METIS is a Sector Skills Alliance project co-funded by the European Commission’s Erasmus+ Program and coordinated by SEMI. The four year project, launched in November 2019, will develop a Microelectronics Skills Strategy. Based on the strategy, the METIS project will design 43 training modules for 1,100 hours learning in four key areas of the microelectronics sector.We thank Patrick Blouet (STMicroelectronics) and Jeroen Geusens (imec) for their valuable contributions to this article.[1] Ethics of Artificial Intelligence and Robotics, Stanford Encyclopedia of PhilosophyDr. Yanying Li is senior manager of Collaborative Projects at SEMI Europe.Dr. Pushkar P. Apte is the strategic technology advisor for the Smart Data AI Initiative at SEMI

Back in February of this year, we launched SEMI Works™, a landmark SEMI program designed to grow and sustain the electronics industry talent pipeline from the ground up. But it was much more than a program launch. The introduction was a resounding statement of our passionate commitment to workforce development and its incontrovertible importance to the future of the microelectronics industry. No one’s passion for workforce development burns brighter than SEMI CEO Ajit Manocha’s. In April, he reiterated SEMI’s focus to make good on this commitment and laid out the broad outlines of SEMI Works. From the outset, our sights have been firmly fixed on execution. The National Science Foundation (NSF), a United States government agency that supports fundamental research and education in science and engineering, recently lent its support to SEMI Works with a $6 million investment to develop a scalable, sustainable apparatus to meet current and future talent requirements of the end-to-end electronics manufacturing industry. And more financial backing – this time from abroad – could well be in the offing. We are pressing ahead to develop the infrastructure to connect talent, industry and education providers at scale. We are expanding proven programs for exciting and engaging students in experiential learning opportunities at a young age. And we are paving the way to offer career and educational pathways through high school, college and adult and veteran training. Regional partners are essential to scaling these programs, and to date we have identified three regions for pilots to develop the infrastructure and business model that will be heartbeat of SEMI Works.Moore’s Law is losing steam, raising hard questions about the semiconductor industry’s ability to maintain its swift pace of innovation. The clarion call for chipmakers is to design ever smaller electronic circuits with higher processing power for devices with shrinking form factors. More computing muscle is crucial to advances in smart manufacturing, medtech, quantum computing, artificial intelligence (AI), 5G and the IoT – all technologies that generate and consume staggering amounts of data.Yet no obstacle to industry growth stands as tall as the brick wall of the talent shortage. A highly skilled workforce is essential to invention. As an industry, we’ll only be equal to the world’s greatest challenges by recruiting, training and retaining the best and brightest.At this critical juncture in what is the world’s most strategic industry, the public and private sectors must work collaboratively to leverage their collective strength to produce the talent required to power technology development today and well into the future.In 2020 SEMI will mark 50 years of facilitating collaborations to mint new technologies and markets. We are uniquely positioned, with our members, to lead what history may one day record as our most important effort to date, a push that could impact the world for decades to come. The industry needs a lasting solution to expand and sustain its talent pipeline. SEMI is taking decisive action with SEMI Works. Mike Russo is vice president of Global Industry Advocacy at SEMI.

In the long unfolding arc of technology innovation, artificial intelligence (AI) looms immense. In its quest to mimic human behavior, the technology touches energy, agriculture, manufacturing, logistics, healthcare, construction, transportation and nearly every other imaginable industry – a defining role that promises to fast track the fourth Industrial Revolution. And if the industry oracles have it right, AI growth will be nothing shy of explosive.“The gains these days are not incremental,” said Ajit Manocha, SEMI president and CEO, said to a gathering in July of the Chinese American Semiconductor Professional Association (CASPA) for its Summer Symposium at SEMI’s headquarters in Milpitas. “They are hockey stick – exponential – with AI semiconductors growing in market size from $4 billion this year to $70 billion in 2025.”Manocha left little doubt that AI is remaking the semiconductor industry and, in the process, the world at large. Internet of Things (IoT) and 4G/5G, both key AI enablers, will account for more than 75 percent of device connections by 2025.“Today, 30 billion devices worldwide are connected,” Manocha said, citing an Applied Materials prediction that the number of connected devices globally will grow to between 500 billion and 1 trillion by 2030. Those devices will generate stunning amounts of data collected, interpreted and used to reason, solve problems, learn and plan, leading to the holy grail of autonomous machine behavior.To process this colossal amount of data central to the promise of AI, the industry must break through the limits of a key technology: memory. Memory a Critical AI BottleneckThe challenge for memory starts with performance. Historically, every decade gains in compute performance have outpaced improvements in memory speed by 100 times, and over the past 20 years that gap has grown, said Steven Woo, a fellow and distinguished inventor at Rambus, presenting at the symposium. The upshot is that memory has bottlenecked compute and, in turn, AI performance. The industry has responded with new ways to implement memory systems on AI chips. Each is suited to unique performance requirements and, of course, comes with trade-offs. Among the frontrunners: On-chip memory delivers the highest bandwidth and power efficiency but is limited in capacity. HBM (High Bandwidth Memory) offers both very high memory bandwidth and density. GDDR balances trade-offs among bandwidth, power efficiency, cost and reliability. Since 2012, AI training capability has grown 300,000 times, besting Moore’s law by 25,000 times in doubling every 3.5 months, a blistering pace compared to the 18-month doubling cycle of Moore’s law, Woo said. The staggering improvements have been driven by parallel computing capacity and new application-specific silicon like Google’s Tensor Processing Unit (TPU).These specialized silicon architectures and parallel engines are key to sustaining future gains in compute performance and combatting the slowing of Moore’s Law and the end of power scaling, Woo said. By rethinking the way processors are architected for certain markets, chipmakers can develop dedicated hardware capable of operating with 100 to 1,000 times greater energy efficiency than general purpose processors to overcome another big limiter to scaling compute performance – power.For its part, the memory industry can improve performance by signaling at higher data rates and using stacked architectures like HBM for greater power efficiency and performance, and by bringing compute closer to the data.Memory scaling for AIA key challenge is scaling memory for AI. Demand for better voice, gesture and facial recognition experiences and more immersive virtual reality and augmented reality interactions is tremendous, said Bill En, senior director at AMD, speaking at the symposium. These capabilities require more processing power across both high-performance computing (HPC) for big data analytics and machine learning as it relies on AI and machine intelligence to generate meaningful insights. Emerging machine learning applications include classification and security, medicine, advanced driver assistance, human-aided design, real-time analytics and industrial automation. And with 75 billion IoT-connected devices – all generating data – expected by 2025, there will be no shortage of data to analyze, En said. The wings alone of a new Airbus A380-1000 feature some 10,000 sensors.Mountains of this data are stored in massive data centers on magnetic hard drives, then transferred to DRAM before moving to SRAM within the CPU for the handoff to the compute hardware for analysis.With data growing at an exponential clip, the question is how to make sure all other memory systems can handle the flood of data. AMD’s answer is a chiplet architecture featuring eight smaller chips around the edge that drive the compute and a large chip in the center that doubles the IO interface and memory capability to in turn double chip bandwidth.AMD has also moved from a legacy GDDR5 memory chip configuration to HBM to bring memory bandwidth closer to the GPU for more efficient processing of AI applications. The HBM provides much higher bandwidth while reducing power consumption. Compared to DRAM, AMD’s HBM delivers a much faster data rate and far greater memory density, En said.Over the next decade, look for more performance improvements from multi-chip architectures, innovations in memory technology and integration, aggressive 3D stacking and streamlined system-level interconnects, he said. The industry will also continue to drive performance gains in devices, compute density and power through technology scaling.Michael Hall is a global marketing communications manager at SEMI.

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.

How are flexible electronics impacting the automotive sector? How will medical diagnostics and life sciences be changing with the advent of flexible, conformable electronics? How does space exploration intersect with the continued development of flexible sensors and Internet of Things (IoT) systems? The upcoming 2019FLEX Japan / MEMS SENSORS FORUM in Shinagawa, Tokyo, May 22-23, 2019, will explore these questions and more. The event, the third FLEX Japan, is expected to gather 300 designers, technologists, researchers, analyst and product developers to hear presentations, discuss their approaches, and create connections. The transformation of the automotive industry will receive special attention with speakers from Yole Développement and a deep exploration of the new sensor form factors and capabilities. Professor Shoji Kawahito of Shizuoka University will discuss the impact of image sensors on automotive LIDAR, night vision and monitors for the driver and passengers. Dr. Yoshifumi Sakamoto of IBM Japan will share his views on key trends in smart transportation and what they mean for the supply chain. Beck Oh, president and CEO of PNI Sensor, will share how parking sensors are transforming our driving – and parking – experience. Hideo Fukunaga, project manager for Velodyne LiDAR, will discuss his work using LIDAR, often seen as the most promising and the most difficult and expensive component of autonomous driving. Jerome Joimel, CTO of ISORG, will discuss integration of organic image sensor behind display.Medical and home electronics devices are moving out of their boxes and hospitals, and flexible electronics, new sensor designs and new power options are playing a major role in that transformation. Jenax, Kobe University, Toyo University, Osaka University, and Daiwa House are just some of the presenters in this area. Researchers are steadily overcoming key technology hurdles, such as electronic interconnects between soft and rigid surfaces, and energy harvesting techniques for no-power devices, as well as ultra-thin RF components, and advanced microfluidic systems. Space, the final frontier, will be the backdrop for the general keynote talk of Mayya Mayyappan, chief scientist for exploration technology at NASA’s Ames Research Center. His team is investigating new printed and flexible sensors and electronics that can be printed in zero-gravity and how these devices will enable IoT.The only event in Japan focused on flexible and printed electronics, with special focus on the complementary areas of sensors and MEMS, 2019FLEX Japan / MEMS SENSORS FORUM provides an excellent opportunity to meet with industry players considering integration and application of new form factor electronics. More than 20 exhibitors will showcase the building blocks for conceptualizing and designing new products immediately.Register now!

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.