Artificial Intelligence

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

Kyushu, the third largest island in Japan, is home to the semiconductor production bases of integrated device manufacturers (IDMs) with world-class cutting-edge technology. SONY, Toshiba, Hitachi, Mitsubishi, Fujitsu and Nissan are among the sector’s shining stars, though a host of other IDMs tied to the supply chains of other major enterprises have also set root in Kyushu. Collectively, the companies earned Kyushu the name Silicon Island of Japan.Kyushu’s flourishing IDM industry sprouted from favorable tax and other government policies that reduced semiconductor production costs to levels lower than elsewhere in Japan. Once the IC producers had established bases, equipment and materials companies naturally followed, leading to the influx of many parts manufacturers. Together, they came to Kyushu, one after another, to make the island a magnet for manufacturing. And so it was to Kyushu that a SEMI China delegation travelled for a meeting at TEL’s factory in Kumamoto to learn more about the secrets to the rapid growth of the island’s semiconductor industry and promote cooperation between Chinese and Japanese enterprises. Underscoring the rise of the Silicon Island of Japan, China will soon become TEL’s largest market, said Masami Akimoto, Chairman of Tokyo Electron Kyushu Limited, speaking at the event. Masami Akimoto hopes for support from SEMI China.The island of 12 million people contributes to the growth of the global semiconductor industry, expected to reach USD 500 billion in size in 2019 as China’s semiconductor sector, fueled in part by government-backed investment funds, continues its rapid expansion. Despite the gains, China still lags other regions in advanced manufacturing, said Lung Chu, president of SEMI China, which is doing its part to draw more advanced manufacturing to the region through its SIIP platform. The initiative encourages pan-regional cooperation with China’s semiconductor industry to promote free trade, open markets, technology innovation and IP protection – all to help China better integrate with the global semiconductor industry. SEMI China President Lung Chu(L) issues visit memorial to Masami Akimoto(R), Chairman of Tokyo Electron Kyushu Limited. Chicken shall be led by the HenUnlike other regions with comprehensive semiconductor industries, Kyushu’s is primarily focused on production and assembly, with more than 200 manufacturers of semiconductor equipment and parts.SEMI China Delegation at Tokyo Electron Kyushu LimitedTEL built its first factory in Kumamoto, a city covered by volcanic ash in the center of Kyushu, 34 years ago. Today, TEL every month produces 80 to 90 sets of equipment, each consisting of, on average, over 400 thousand parts that must be certified and authorized by TEL before delivery to its module manufacturers and assembly into complete machines. Having blossomed over the past few decades, the island’s supply chain now supplies TEL with all its equipment parts. SEMI China Delegation at Fajita WorksTEL supplier Fajita Works, a high-precision plate metal manufacturer founded in 1945, is emblematic of other companies in the Kyushu supply chain. It keeps a low public profile as it serves several longtime customers and earns ardent loyalty from its workers, an ethos reflected in the change next January of its slog from “Only One” to “Great company, Great life.”Quality is the life of the enterpriseLong before the rise of its legendary automobile and consumer electronics companies, Japan was known for inferior, counterfeited products, labeled “Made In USA” and shipped to the United States by more than 100 factories. The net effect was to shrink and commoditize American markets. The tide in Japan’s product quality and stained reputation began to turn in the 1980s, when Japan’s semiconductor industry began to produce memory with an error rate 27 times lower than its U.S. competitors, giving Japan an upper hand in quality that it would never relinquish. SEMI China Delegation at HORIBAKyushu-based flowmeter supplier HORIBA, among the many Japanese companies famous for their product quality, ships 38 percent of its products into the automotive market and 27 percent into the semiconductor sector. Cleanliness is as vital a part of the company’s culture as quality. Each depends on the other, with fine detail held to the highest importance. On its visit to HORIBA, the SEMI China delegation, passing by an office area before entering the factory, sighed at the sight of the spotless, neatly kept furniture and workspace: They had never seen an office so sparkling clean. HORIBA’s success is rooted in immaculate offices, factories and the company’s motto “Enjoy innovation and pay close attention to product quality.”After Kumamoto sustained heavy damage during a 2016 earthquake, HORIBA workers returned rocks scattered by temblor to their original position, knowing that order is critical to lean, efficient manufacturing and that, indeed, “the devil is in the details.” SEMI China Delegation in Kumamoto City Full confidence in the exploration of Chinese marketConsumer electronics stalwarts Sony and Panasonic feature semiconductor factories in Kagoshima, the southernmost city in Kyushu and Japan, though rumor had it two years ago that Panasonic planned to pull out. The Panasonic plant, which provides batteries for Tesla, remains. The Sony facility produces image sensors for the iPhone.Semiconductor equipment maker ULVAC, SEMI China’s most important strategic partner, is also based in Kagoshima. During the delegation’s visit to the company, Lung Chu noted that while China is the world’s largest semiconductor market, the region meets just 13 percent of domestic chip demand. Stressing that ULVAC can play a crucial role in helping China become a bigger player, he expressed admiration for ULVAC’s professionalism along with hope that it will maintain its rapid growth and leverage SEMI resources to catalyze rapid development of Internet of Things (IoT), artificial intelligence (AI), and 5G technologies in China and rise into the top 10 of global equipment manufacturers. SEMI China President Lung Chu (L) issues visit memorial to ULVAC Kyushu President and CEO Kenji Yamaguchi ULVAC Kyushu president and CEO Kenji Yamaguchi made clear the company’s interest in Lung Chu’s insights into Chinese semiconductor industry while underscoring its core competency of producing semiconductors for flat panel displays. The Kyushu Factory of ULVAC is full of vitality and market competitiveness. SEMI China Delegation at ULVAC EBARA, a precision machinery company located in Kumamoto, has manufactured chemical-mechanical planarization (CMP) equipment for over 20 years and delivered nearly 2,400 mechanical polishing machines worldwide. While the company expects to ship 50 sets per year to China starting next year, it has the capacity to deliver 20 sets per month, enough to meet demand of Chinese semiconductor makers. SEMI China Delegation at EBARAThe most telling takeaway from the SEMI China delegation’s visit to the Kyushu: Japan ranks number one worldwide in research and development (R D) investment as a proportion of GDP and is also at the top in the percentage of R D funds controlled by private enterprises. The outsize investment strategy has enabled Japan to maintain its hold as one of the world’s top technology innovators.Like Sakurajima, the famed Kyushu volcano, the SEMI China delegation will continue to harness its forces to build relationships with the island’s semiconductor supply chain as it works to develop win-win pan-regional relationships and foster the growth of China’s semiconductor industry. Best view of Sakurai volcano Gang Yao is a marketing director at SEMI China.

Marcellino Gemelli, director of global business development at Bosch Sensortec, will present at the upcoming MEMS Sensors Executive Congress on October 29-30, 2018 in Napa, Calif. SEMI’s Maria Vetrano caught up with Gemelli to give MSEC attendees a preview of Gemelli’s feature presentation.Sensor fusion — the integration of different types of sensors through software algorithms to increase overall system performance and/or reduce power consumption— has come a long way since its inception. In those early days, sensor fusion generally involved MEMS inertial sensors only. The advent of new sensor varieties, including environmental sensors, is making new use cases a reality. Gemelli will explore the ways in which the next generation of sensor fusion is improving autonomous mobility devices. SEMI: Why are environmental sensors important to autonomous mobility devices?Gemelli: When most of us think of autonomous systems, we think that they are driven by motion sensors and proximity sensors (e.g., radar, Lidar). When vertical location comes into play, however, in applications such as drones or asset tracking, pressure sensors become an integral part of flight control, navigation and positioning in GPS-challenged areas.While not commonly considered an electronically enabled sense, the ability to “smell” the environment opens new opportunities. The quality of a user’s experience with personal cleaning robots and robo-taxis are good examples of where we might want to enable scent detection.SEMI: I’ve never thought much about using sensors to detect smell. How would a robo-taxi or a cleaning robot benefit from scent detection?Gemelli: Fully autonomous cars will inevitably give rise to robo-taxis. In fact, last month Volvo announced its fully electric robo-taxi, and in March 2018 Waymo announced that Jaguar Land Rover’s SUV would join Fiat Chrysler’s Chrysler Pacifica minivans in its planned fleet of robo-taxis, so we may see robo-taxis in the U.S. within the next five years.With robo-taxis fast-approaching, we need technologies that provide the same level of oversight that a taxi driver once fulfilled. Gas sensors would function like an electronic nose (e-nose) in a robo-taxi to inform the taxi’s owner of prohibited passenger behavior, such as eating, drinking or smoking in the vehicle, which could potentially damage the vehicle’s interior. Camera sensors could record the act as proof of the offense.Cleaning robots would be more sophisticated than they are today. In addition to leveraging image and range-finding sensors to more accurately map the rooms in your house, they could also detect scents from spilled red wine, pet urine or other foreign materials. When the cleaning robot, such as a vacuum, detects the foreign substance, it would navigate around the substance instead of going through it and spreading it all over the carpet.In addition to robo-taxis and cleaning robots, I will also discuss asset tracking and drones.SEMI: What role does sensor fusion play in autonomous mobility devices?Gemelli: Combining sensor fusion with artificial intelligence (AI) will generate new use cases and therefore new markets for sensor suppliers.There is another major benefit as well. With so many connected devices in our lives — including those with cameras, location awareness and always-listening capabilities — we are seeing growing concern about user privacy. Sensor fusion and AI can help to alleviate this concern: By supporting more local processing, they allow for greater control of data, safeguarding personal privacy.SEMI: Who is responsible for the AI part of the sensor-fusion equation?Gemelli: AI is a new frontier for MEMS and sensors suppliers. It benefits us and our customers to embrace AI algorithms through in-house development and/or partnerships.SEMI: What would you like MEMS Sensors Executive Congress attendees to take away from your presentation?Gemelli: I plan to issue a call to action to increase research in hybrid sensor-fusion software architectures, including AI, as suppliers’ collaboration will benefit the industry at large.Marcellino Gemelli is currently based in Palo Alto (CA) responsible for business development of Bosch Sensortec's MEMS product portfolio. He received the ‘Laurea’ degree in Electronic Engineering at the University of Pavia, Italy while in the Italian Army and an MBA from MIP, the Milano (Italy) Polytechnic business school. He previously held various engineering and product management positions at STMicroelectronics from 1995 to 2011 in the fields of MEMS, electronic design automation and data storage. He was contract professor for the Microelectronics course at the Milano (Italy) Polytechnic from 2000 to 2002.Marcellino Gemelli will present Environmental Sensors Systems Enabling Autonomous Mobility on Tuesday, October 30 at MEMS Sensors Executive Congress in Napa Valley, Calif.Register today to learn more about the connection between sensor fusion, AI and next-generation autonomous mobility devices.Maria Vetrano is a public relations consultant at SEMI.

Korea is on track to top all other regions in fab investment, spending $63 billion between 2017 and 2020, with powerhouses Samsung Electronics Co. and SK Hynix leading the way, according to latest World Fab Forecast Report by SEMI. Samsung Electronics increased fab investments $770 million to $12 billion this year, and SK Hynix upped its spending a significant $2.8 billion to $7.25 billion in 2018.Korea's investment companies anticipate continued growth for both companies in the second half of 2018.Under this halo of extraordinary investment, nearly 380 SEMI Korea members and industry analysts gathered for 2018 SEMI Korea Members Day on September 13 to share insights on semiconductor market trends and new technologies that could help members bolster their competitiveness. Following are key takeaways from the event. Korea semiconductor market to grow 16% in 2018That’s according to IDC Korea VP Kim Soo-kyung, who noted that data center, memory and Internet of Things (IoT) are becoming key growth drivers for the semiconductor industry. He encouraged semiconductor companies to closely track development of automotive technology and the industry semiconductor market, both key growth areas. SEMI Korea president H.D. Cho opens SEMI Korea Members Day 2018 Continuing fab investment will lead to oversupply, but display will shineMarket entry by Chinese companies will also spur the oversupply, said Jeong Won-Seok, an analyst at HI Investment Corp. He noted that the oversupply will force Korea into stiffer competition with other regions. However, with OLED used for a wide variety of devices and the display industry seeing rapid growth, the sector will remain ripe for growth among Korean companies.Interconnecting various applications is a big semiconductor industry trendThe need for these interconnections will stand out in the mobility and high-performance computing (HPC) markets, said Park Sung-Soon, principal research fellow at Amkor Technology Korea, who addressed trends in packaging technology. He also emphasized interconnection cost efficiency as key to maximizing competitiveness.Smart Manufacturing is driving mass customizationAs semiconductor industry growth continues, production methods are shifting from ‘mass production’ to ‘mass customization,’ increasing the importance of Smart Manufacturing in driving greater production efficiency, noted BISTel VP Jeon Kyeong-Sik. Building a Smart Manufacturing platform to support large-scale production of specialized database and artificial intelligence (AI) chips will boost production efficiency, reduce costs and improve risk management. Virtual simulation will be a key enabling technology. SEMI analyst Clark Tseng presenting at SEMI Korea Members Day 2018 Surge in data volume and technology advances to drive long-term semiconductor industry growthThese key industry drivers will continue to power fab investment growth, with spending focused on 3D NAND, DRAM, and foundry, said Clark Tseng, director of Industry Research and Statistics at SEMI. China alone will see eye-watering growth with the region’s investments in domestic companies surging 46% from 2018 to 2019 and fab investment by Chinese domestic companies outpacing spending by foreign companies in China, Tseng predicted. SEMI membership rises with industry growthCulminating the event, SEMI Korea president H.D. Cho said, "With the growth of the semiconductor market, the number of SEMI members is gradually increasing, and we will help member companies grow with various activities such as Korea Members Day.”Jaegwan Shim is a marketing specialist at SEMI Korea.

2017 was a good year for the MEMS and sensors business, and that upward trend should continue. We forecast extended strong growth for the sensors and actuators market, reaching more than $100 billion in 2023 for a total of 185 billion units. Optical sensors, especially CMOS image sensors, will have the lion’s share with almost 40 percent of market value. MEMS will also play an important role in that growth: During 2018–2023, the MEMS market will experience 17.5 percent growth in value and 26.7 percent growth in units, with the consumer market accounting for more than 50 percent(1) share overall. Evolution of SensorsSensors were first developed and used for physical sensing: shock, pressure, then acceleration and rotation. Greater investment in R D spurred MEMS’ expansion from physical sensing to light management (e.g., micromirrors) and then to uncooled infrared sensing (e.g., microbolometers). From sensing light to sensing sound, MEMS microphones formed the next wave of MEMS development. MEMS and sensors are entering a new and exciting phase of evolution as they transcend human perception, progressing toward ultrasonic, infrared and hyperspectral sensing.Sensors can help us to compensate when our physical or emotional sensing is limited in some way. Higher-performance MEMS microphones are already helping the hearing-impaired. Researchers at Arizona State University are among those developing cochlear implants — featuring piezoelectric MEMS sensors — which may one day restore hearing to those with significant hearing loss. The visually impaired may take heart in knowing that researchers at Stanford University are collaborating on silicon retinal implants. Pixium Vision began clinical trials in humans in 2017 with its silicon retinal implants.It’s not science fiction to think that we will use future generations of sensors for emotion/empathy sensing. Augmenting our reality, such sensing could have many uses, perhaps even aiding the ability of people on the autism spectrum to more easily interpret the emotions of others.Through my years in the MEMS industry, I have identified three distinct eras in MEMS’ evolution: The “detection era” in the very first years, when we used simple sensors to detect a shock. The “measuring era” when sensors could not only sense and detect but also measure (e.g., a rotation). The “global-perception awareness era” when we increasingly use sensors to map the environment. We conduct 3D imaging with Lidar for autonomous vehicles. We monitor air quality using environmental sensors. We recognize gestures using accelerometers and/or ultrasonics. We implement biometry with fingerprint and facial recognition sensors. This is possible thanks to sensor fusion of multiple parameters, together with artificial intelligence. Numerous technological breakthroughs are responsible for this steady stream of advancements: new sensor design, new processes and materials, new integration approaches, new packaging, sensor fusion, and new detection principles.Global Awareness SensingThe era of global awareness sensing is upon us. We can either view global awareness as an extension of human sensing capabilities (e.g., adding infrared imaging to visible) or as beyond-human sensing capabilities (e.g., machines with superior environmental perception, such as Lidar in a robotic vehicle). Think about Professor X in Marvel’s universe, and you can imagine how human perception could evolve in the future! Some companies envisioned global awareness from the start. Movea (now part of TDK InvenSense), for example, began their development with inertial MEMS. Others implemented global awareness by combining optical sensors such as Lidar and night-vision sensors for robotic cars. A third contingent grouped environmental sensors (gas, particle, pressure, temperature) to check air quality. The newest entrant in this group, the particle sensor, could play an especially important role in air-quality sensing, particularly in wearable devices.Driven by increasing societal concern over mounting evidence of global air-quality deterioration, air pollution has become a major topic in our society. Studies show that there is no safe level of particulates. Instead, for every increase in concentration of PM10 or PM2.5 inhalable particles in the air, the lung cancer rate is rising proportionately. Combining a particle sensor with a mapping application in a wearable could allow us to identify the locations of the most polluted urban zones.The Need for Artificial Intelligence To realize global awareness, we also need artificial intelligence (AI), but first, we have challenges to solve. Activity tracking, for example, requires accurate live classification of AI data. Relegating all AI processing to a main processor, however, would consume significant CPU resources, reducing available processing power. Likewise, storing all AI data on the device would push up storage costs. To marry AI with MEMS, we must do the following: Decouple feature processing from the execution of the classification engine to a more powerful external processor. Reduce storage and processing demands by deploying only the features required for accurate activity recognition. Install low-power MEMS sensors that can incorporate data from multiple sensors (sensor fusion) and enable pre-processing for always-on execution. Retrain the model with system-supported data that can accurately identify the user’s activities. There are two ways to add AI and software in mobile and automotive applications. The first is a centralized approach, where sensor data is processed in the auxiliary power unit (APU) that contains the software. The second is a decentralized approach, where the sensor chip is localized in the same package, close to the software and the AI (in the DSP for a CMOS image sensor, for example). Whatever the approach, MEMS and sensors manufacturers need to understand AI, although they are unlikely to gain much value at the sensor-chip level.Heading to an Augmented WorldWe have achieved massive progress in sensor development over the years and are now reaching the point when sensors can mimic or augment most of our perception: vision, hearing, touch, smell and even emotion/empathy as well as some aesthetic senses. We should realize that humans are not the only ones to benefit from these developments. Enhanced perception will also allow robots to help us in our daily lives (through smart transportation, better medical care, contextually aware environments and more). We need to couple smart sensors’ development with AI to further enhance our experiences with the people, places and things in our lives.About the authorWith almost 20 years’ experience in MEMS, sensors and photonics applications, markets, and technology analyses, Dr. Eric Mounier provides in-depth industry insight into current and future trends. As a Principal Analyst, Technology Markets, MEMS Photonics, in the Photonics, Sensing Display Division, he contributes daily to the development of MEMS and photonics activities at Yole Développement (Yole). He is involved with a large collection of market and technology reports, as well as multiple custom consulting projects: business strategy, identification of investment or acquisition targets, due diligence (buy/sell side), market and technology analyses, cost modeling, and technology scouting, etc.Previously, Mounier held R D and marketing positions at CEA Leti (France). He has spoken in numerous international conferences and has authored or co-authored more than 100 papers. Mounier has a Semiconductor Engineering Degree and a PhD in Optoelectronics from the National Polytechnic Institute of Grenoble (France).Mounier is a featured speaker at SEMI-MSIG European MEMS Sensors Summit, September 20, 2018 in Grenoble, France. (1) Source: Status of the MEMS Industry report, Yole Développement, 2018

The proliferation of chips driven by artificial intelligence and automotive will spur a record $68 billion in sales of semiconductor equipment in 2019.Building the workforce of the future is at once a challenge and opportunity for the semiconductor industry, the key to continuing the rapid pace of innovation and growth. And tariffs levied against billions of dollars of Chinese imports by the Trump administration has profound implications for SEMI members and the chip industry as a whole.Jonathan Davis, Global Vice President of Industry Advocacy at SEMI, offers insights on these key semiconductor industry issues in this interview at SEMICON West 2018. Ariana Raftopoulos is a marketing manager at SEMI.

Part 2 of this two-part piece examines the potential benefits to be realized by pairing human Subject Matter Experts with smart silicon assistants, and what these new arrangements mean for semiconductor device manufacturing. Part 1 explores best-practice perspectives on collecting and utilizing smart data in industries outside semiconductor manufacturing, one of the important takeaways from the Smart Manufacturing panel discussion at SEMI ASMC 2018. So what does this observation (i.e. the field of medicine, in what seems at first glance a big data environment, is really just clusters and clusters of loose small data connected by the collective neural network of highly trained doctors and their colleagues) mean for semiconductor manufacturing? We think it means we need to apply the same level of intense focus that we already devote to instrumented data collection and analytics in the fab to something more: we need to better capture the vast expertise of our engineering and operational talent in semiconductor manufacturing. We think we need to record what the subject matter experts (SMEs) in the fab see, hear, and think as they investigate yield excursions or machine-down problems. We need to effectively combine product, process, equipment and component subject matter expertise / subject matter experts (SME) with big data analytics to more effectively solve manufacturing problems, be they killer or be they chronic. And we must provide structured methods for incorporating inputs from and active participation of SMEs throughout the data analysis lifecycle, from collection and aggregation, through filtering, feature extraction, analysis and optimization. Some of the challenge will be in just how do we make it easy to gather information from SMEs in real time, while standing in front of equipment in the fab. Internet of Things (Iot) devices are emerging to capture and label images and sounds to enable machine learning algorithms to recognize and help diagnose manufacturing problems based on sight and sound, complementing the instrumented data. But we also need to record the thought processes our human SMEs go through in those investigations – perhaps by the SMEs talking to a smart AI-based conversational assistant who helps make “rounds.” Doing contextual analysis on this added data, combined with the instrumented data, will create the equation Human + Machine = AI (Awesome Insight). Sounds reasonable, right? We think artificial intelligence becomes too artificial if you leave the human out of the equation. AI should be augmented intelligence, where we take the expertise and creativity of the human, and combine it with the rapid computational capabilities of the computer, in order to put problem identification and solutions on steroids. But with the already huge advancements to date in data analytics, cloud, and the emergence of AI, why do improvements in quality, machine utilization, and the implementation of predictive analytics in semiconductor manufacturing seem to be creeping along incrementally, and not appearing as dramatic, step-function improvements? Call it Smart Manufacturing, call it Connected Enterprise, call it Advanced Manufacturing, or Analytics, or Cloud, or the Digital Twin … there are no shortages of terms, philosophies, and technologies available, but why aren’t we seeing their rapid adoption? It could be it’s the downside that comes with needing people. “Good business leaders create a vision, articulate the vision, passionately own the vision, and relentlessly drive it to completion.” Jack Welch. We see from other industries that smart manufacturing conversations originating with the executives of a company thinking to implement smart manufacturing programs lead to vision; however, we also see from other industries, and from our own, that realizing this vision has often been a challenge. Why is that? One reason may be that the people who are personally vested in solutions they implemented in the past, as well as those who follow a pattern of ‘how we’ve always done things’, create, inadvertently or not, persistent internal barriers hindering innovative action. Another may be that engagements with the working engineers and managers charged to be smart manufacturing implementers leads to the pursuit of low-hanging fruit, and cautious investments, that often utilize solutions that ultimately cannot scale and integrate. Not to mention the disadvantage of dealing with the legacy equipment, the legacy networks, the traditional thinking, and the lack of consistency in metrics adding to the confusion. Addressing all these barriers requires an alignment in strategy and execution, along with a plan to support the overall vision, often across the entire enterprise, which is no small matter. And then there are the standards. Having and adhering to standards in control solutions, networks, and data becomes critical in achieving real benefits from smart manufacturing. And data security. One of the other big impediments in the smart manufacturing transformation is data and IP security, another key concern (maybe the most significant) preventing us from moving forward more quickly (e.g. to cloud-based solutions) in our industry. More about that in a follow-up. Achieving synergy across all of manufacturing, from connecting equipment horizontally, through the production system (machines processes), and vertically, through enterprise systems and across production facilities, can only occur if we build standards, security, infrastructure, and human engagement throughout our ecosystem and supply chain. In simple form, the steps to do so include connecting assets, collecting and contextualizing data, and then driving business transformation with actionable insights gained from the data. With impact on every function, and every person, in the enterprise, from equipment operators in the fab through the C-Suite in HQ. Maintenance, Engineering, R D, Operations, Scheduling, IT, Procurement, Finance, HR all contribute, collaborate and benefit. Regardless of the technology, from device level analytics to predictive maintenance and optimization, the people that reside in these disparate groups need to come together with the smart machines to create a common strategy to achieve transformational results. Aligning an enterprise’s goals with its human capital is paramount to success. Therefore, we must challenge our team members and ourselves to work outside our comfort zones, and we need to be forever aware of the need for us to grow with the technology. Smart manufacturing is not necessarily about having fewer people in the fab, but it does suggest having people in the fab, perhaps with different, or upgraded, skill sets, who are even more efficient in their roles as a result of the boost they are getting from Industry 4.0. Fortunately, we now have techniques that let us combine the best, brightest, and latest and greatest analytics with our invaluable SMEs throughout the data analysis lifecycle. We’ll not only be able to deliver higher quality semiconductor manufacturing solutions all in all, but we’ll also be providing methods to more easily distribute, scale, maintain, and continually refine those hard-earned solutions. We expect that subject matter experts will continue to put the “smart” in machine-based smart manufacturing today, and for the foreseeable future. SME contributions are not an option, but, rather, an imperative for ensuring a semiconductor manufacturer’s sustained prosperity, much less its survival. Nancy Greco (IBM Watson), Dave Mayewski (Rockwell Automation), James Moyne (University of Michigan / Applied Materials), and Paul Werbaneth (Intevac, Inc.), along with Julie Jacob (Ernst Young), and Carson Henry (Micron Technology), were members of the SEMI ASMC 2018 panel discussing Industry 4.0 and the Future of Commercial Semiconductor Device Manufacturing. All opinions here are purely our own. Please contact Paul Werbaneth via email at [email protected]. The SEMICON West (July 9-11, 2018, in San Francisco) Smart Manufacturing Pavilion features working production equipment on the floor and three full days of speakers providing insights on building the infrastructure needed to enable AI. Equipment from Bosch Rexroth, Cimetrix, Rudolph Technologies, INFICON, Final Phase Systems, OMRON, DISCO and Edwards Vacuum will showcase cutting-edge smart manufacturing technologies. For information on the SEMI Smart Manufacturing initiative and how to get involved, please click here.

Part 1 of this two-part piece explores best-practice perspectives on collecting and utilizing smart data in industries outside semiconductor manufacturing, one of the important takeaways from the Smart Manufacturing panel discussion at SEMI ASMC 2018. Part 2 examines the potential benefits to be realized by pairing human Subject Matter Experts with smart silicon assistants, and what these new arrangements mean for semiconductor device manufacturing. The spacecraft Discovery and its HAL 9000 computer system had a digital twin. Did you know? Stanley Kubrick’s seminal film “2001: A Space Odyssey” had its theatrical release 50 years ago this April. “2001” isn’t just a great science fiction film. Rather, it’s a great work of cinema overall, across any category. (The American Film Institute lists “2001” as #15 in the AFI Top 100; a bit below “Vertigo,” a bit above “It’s A Wonderful Life.”) It’s a film so distinguished and so prescient that its lessons can inform our thinking about smart manufacturing, Industry 4.0, and artificial intelligence (AI) today. Not to give too much away, but the earth-bound digital twin of Discovery / HAL identifies a diagnostic error the onboard, Jupiter-bound HAL 9000 has made, things go awry from there, and one of the mission pilots, astronaut Dave Bowman, is forced to intervene. At the recent SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2018, on 02 May 2018 in Saratoga Springs, NY, five diverse panelists representing capital equipment, IDMs, academia, the semiconductor supply chain, and smart manufacturing best practices outside the semiconductor industry engaged in a lively discussion with the ASMC attendees. They explored where “smart” is in our industry today, where it’s headed, and what that’s going to mean for us -- the professionals who have brought semiconductor manufacturing to the current state of smart, and are looking to implement an ever-smarter tomorrow. Not to give too much away, but the panelists and audience agreed that there’s nothing artificial about pairing human intelligence with machine-based smart manufacturing. Implementing an ever-smarter tomorrow in semiconductor manufacturing requires smart people just as much as it requires smart machines. Moving towards “smart” means understanding how to derive useful information and actionable intelligence from the ever-increasing pool of big data created during semiconductor manufacturing. Modern manufacturing sites are extensively instrumented today, and create massive amounts of data to consume, decipher, base decisions upon, or discard. As we dig into this problem we realize that equipment and processes in our industry are both obviously complex, but, also, subtly complex. Semiconductor manufacturing tools easily contain 100s to 1000s of components working together to produce nanometer scale, angstrom scale, or even atomic scale features using complex chemical, physical, and plasma processes. There is a plethora of potential failure points and modes, and despite our best efforts to collect more data, many processes continue to be only poorly observable. On top of that, semiconductor fabrication processes are always drifting, and the operational context is continually changing as we change product mix, process maintenance swap-out kit components, and operating conditions and recipes. Sounds like … hospitals, and healthcare? When you see your doctor, she will collect and look at your instrumented data – blood work, blood pressure, weight, and other quantifiable factors. But, typically, your doctor won’t draw a conclusion based on that analysis alone. Rather, your doctor will sit with you, ask probing questions, and record what she asked, your responses, and what she saw, what she heard, and what she thought. Then she’ll build a hypothesis, combining the “anecdotal” data with the instrumented data, and derive from that data set both a likely diagnosis and an effective course of action. In this case, beyond the instrumented data, two humans, and their natural language input, are part of the equation: the patient, with his observations and thoughts, as well as the doctor, with hers. And it’s been a formula for success. Healthcare has made huge, step-function improvements across a spectrum of deadly diseases, as well as with less-deadly chronic afflictions, by harvesting this complex input, committing the proven disease presentation – disease diagnosis – and disease treatment models to medicine’s collective memory, and then teaching the next generation of healthcare providers both the general methods and the standard protocols essential to maintaining good health and successful outcomes. Maybe, in medicine, what seems a big data environment is really just clusters and clusters of loose small data connected by the collective neural network of highly trained doctors and their colleagues. Nancy Greco (IBM Watson), Dave Mayewski (Rockwell Automation), James Moyne (University of Michigan / Applied Materials), and Paul Werbaneth (Intevac, Inc.), along with Julie Jacob (Ernst Young), and Carson Henry (Micron Technology), were members of the SEMI ASMC 2018 panel discussing Industry 4.0 and the Future of Commercial Semiconductor Device Manufacturing. All opinions here are purely our own. Please contact Paul Werbaneth via email at [email protected]. The SEMICON West (July 9-11, 2018, in San Francisco) Smart Manufacturing Pavilion features working production equipment on the floor and three full days of speakers providing insights on building the infrastructure needed to enable AI. Equipment from Bosch Rexroth, Cimetrix, Rudolph Technologies, INFICON, Final Phase Systems, OMRON, DISCO and Edwards Vacuum will showcase cutting-edge smart manufacturing technologies. For information on the SEMI Smart Manufacturing initiative and how to get involved, please click here.

Do you email your doctor when you have a tickle in your throat? Wear a fitness tracker or use an app to monitor your sugar levels, exercise or nutrition? If so, congratulations! You are a part of the rapid growth of digital medicine.Since you’re on the leading edge of this trend to enhance the efficiency of healthcare delivery, you might enjoy learning more about how the medical industry is transforming healthcare in this collection of podcast episodes and video. These are my top picks and just the tip of the proverbial iceberg in how modern medicine is taking today’s technology and applying it to best practices for remote patient monitoring, medical diagnosis, rural healthcare and more.Enjoy! And let me know if you find any others worth the listen!1. Inside Angle: 3M Health Information Systems - Telemedicine: Enhancing Access to Improve OutcomesAccess to healthcare can be a matter of life or death. For some patients, this may mean taking a day off work and driving for hours because services are not available in their hometown.Inside Angle host Dr. Gordon Moore interviews Barb Johnston, co-founder and CEO of HealthLinkNow, about the implementation of telemedicine programs. They discuss how technology impacts telemedicine adoption and related regulations and benefits clinicians and patients in the telepsychiatry and mental health industry. 2. NPR’s The Salt: What’s On Your Plate – This Chef Lost 50 Pounds and Reversed Prediabetes With A Digital ProgramThis short audio clip and article dives into lifestyle and wellness apps designed to motivate users to eat healthier, exercise more and – in some cases – save them from a preventable disease, like diabetes.Wellness apps like Omada Health rely on smartphones, e-coaching, electronic nudging and other methods to encourage users to make and, more importantly, stick to changes that can improve their health. And it’s catching on as other organizations, such as The Centers for Disease Control and Prevention, recognize the difference lifestyle-change programs designed to prevent diabetes are making. 3. Red Hot Healthcare: Episode 57 - Bluer Skies for TelemedicineLast year alone, venture capitalists poured $7 billion into telemedicine. As an emerging concept, telemedicine has a long road ahead until it’s fully integrated and adopted into modern medical practices. Nathaniel Lacktman, partner and health care lawyer with Foley Lardner LLP, discusses legal issues and hurdles surrounding telemedicine today with Red Hot Healthcare host Dr. Steve Ambrose.Lacktman pointed to healthcare providers such as Mercy Virtual Care Center, Mayo Clinic and MDLive that are likely to lead the telemedicine race. This podcast episode is a great listen for entrepreneurs and medical professionals who are interested in learning about compliance and business considerations for the implementation of telemedicine. 4. Digital Health Today: Episode 56 – Eren Bali on Building the World’s Largest Connected Care NetworkEren Bali, CEO and co-founder of Carbon Health, is out to change our fragmented traditional healthcare system with his aim to build the world’s largest connected care network.For Bali, it all started with his sister’s experience consolidating his mother’s health records – scattered over 20 different systems. And from that challenge, his idea to create a universal network that aggregates patient medical records was born.Bali and Digital Health Today host Dan Kendall discuss Bali’s launch of Carbon Health, the new medical records system’s first implementation in a San Francisco primary care clinic, and how providers and patients can get on board with this model. 5. Digital Health Today: Episode 58 – Brennan Spiegel Gets Real About Virtual RealityThis episode of Digital Health Today centers on how Virtual Reality (VR) is being used to enhance patient care. Host Dan Kendall speaks with Dr. Brennan Spiegel, Director of Health Services Research at Cedars-Sinai Health System and Professor of Medicine and Public Health at UCLA, about how immersive technology like VR and Augmented Reality (AR) can improve patients’ experience as well as alleviate pain, anxiety, depression, addiction and more. In a particularly interesting segment, Dr. Spiegel calls the hospital a biopsychosocial jail cell and underscores its importance in not just treating physical ailments but, more wholistically, also addressing the psychological and social wellbeing of patients. 6. TED2017: Raj Panjabi – No one should die because they live too far from a doctor What do you do when access to a doctor means rowing a boat for hours? Millions of people around the world lack access to health care because they live in a remote town or village.In this TED talk, Raj Panjabi, physician in the Division of Global Health Equity at Harvard Medical School, Brigham and Women’s Hospital, and co-founder of Last Mile Health, offers a solution to the problem of healthcare access in the form of the Community Health Academy, a global platform to train and connect community health workers by leveraging devices like smartphones to bring preventative healthcare to even the most far-flung regions of the world. 7. GeekWire’s Health Tech Podcast: How AI is making humans the ‘fundamental thing in the internet of things’Can AI predict which patients are at risk for chronic diseases using their old medical records? This episode of Health Tech Podcast dives deep into the future of healthcare with a look at the potential for AI -- “augmented intelligence” or “assistive intelligence” – to improve patient outcomes, the focus of Ankur Teredesai, a data scientist at the University of Washington and co-founder and CTO of health AI startup KenSci.Clare McGrane, host of GeekWire’s Health Tech Podcast, speaks with numerous experts in this field. They compare precision health to a modern car constantly monitored by microprocessors. The minute something is wrong, you get a warning to take the car to a shop to resolve the issue. The same concept can be applied to humans and deliver big healthcare impacts as research in AI and machine learning continues to evolve. 8. NPR’s Shots: Can Home Health Visits Help Keep People Out Of The ER?In Washington D.C., the city with the highest per capita 911 call volume in all of the U.S., Mary’s Center is piloting a program to provide primary care telemedicine to patients who cannot, or in some cases, do not want to visit the clinic. NPR covers the story of Medicaid patient Dennis Lebron Dolman, who is currently receiving a mix of home visits and virtual treatment.Besides providing healthcare access to rural regions, telemedicine has the potential to reduce emergency room visits in cities as well as improve the health of patients in the long run. Learn more at SEMICON West’s Smart MedTech TechXPOT on Digital Medicine and Remote Patient Monitoring. Hosted by NBMC on July 12, 2018, from 10:30 AM to 12:30 PM, the event will feature healthcare industry leaders exploring state-of-the-art healthcare practices and the future of medical technology. Registration is now open!Amy Ly is a Technical Programs Marketing Coordinator at SEMI.

Artificial intelligence (AI) is making headlines everywhere, offering a range of capabilities, including location and motion awareness — determining whether a user is sitting, walking, running or sleeping. Behind the scenes, AI is capturing volumes of data. Makers of smartphones and fitness and sports trackers, along with application developers, are all clamoring for this data because it helps them analyze real-world user behavior in depth. Manufacturers gain a competitive edge by tapping this intelligence: Using it to improve user engagement, they increase the perceived value of their devices, potentially reducing customer churn. How can consumer-product manufacturers tap the built-in capabilities of MEMS inertial sensors — which are already ubiquitous in end-user devices — to make the most of AI? Machine learningProduct manufacturers can easily build an activity classification engine using commonly available smart sensors and open-source software. Activity trackers, for example, use raw data first collected via the MEMS inertial sensors that are already installed in smartphones, wearables and other consumer products.With the building blocks in place, consumer-product manufacturers can apply machine learning techniques to classify and analyze this data. There are several possible approaches, ranging from logistic regression to deep learning neural networks.One well-documented method used for classifying sequences in AI is Support Vector Machines (SVM). Physical activities, whether walking or playing sports, consist of specific sequential repetitive movements that MEMS sensors gather as data. MEMS sensors make good use of this collected data, which can be easily processed into well-structured models that are classifiable with SVMs.Consumer-product manufacturers have gravitated toward the SVM model since it is easy to use, scale and predict. Using an SVM to set up multiple simultaneous experiments for optimizing classification over diverse, complex real-life datasets is far simpler than with other approaches. An SVM also introduces a wide range of size and performance optimization opportunities for the underlying classifier.Cost impacts of processing, storage and transmissionIn practice, recognizing user activity hinges on accurate live classification of AI data. Therefore, the key to optimizing product cost is to balance transmission, storage and processing costs without compromising classification accuracy.This is not as simple as it sounds. Storing and processing AI data in the cloud would leave users with a substantial data bill. A WiFi, Bluetooth or 4G module would drive up device costs and require uninterrupted internet access, which is not always possible.Relegating all AI processing to the main processor would consume significant CPU resources, reducing available processing power. Likewise, storing all AI data on the device would push up storage costs.Resolving the issuesTo resolve these technology conflicts, we need to do four things to marry the capabilities of AI with MEMS sensors.First, decouple feature processing from the execution of the classification engine to a more powerful external processor. This minimizes the size of the feature processor size while eliminating the need for continuous live data transmission.Next, reduce storage and processing demands by deploying only the features required for accurate activity recognition. In one example created by UC Irvine Machine Learning Repository (UCI), when an AI model was trained using a dataset of activities with 561 features, it identified user activity with an accuracy of 91.84 percent. However, using just the 19 most determinative features, the model still achieved an impressive accuracy of 85.38 percent. Notably, pre-processing alone could not identify these determinative features. Only sensor fusion enabled the data reliability required for accurate classification. Third, install low-power MEMS sensors that can incorporate data from multiple sensors (sensor fusion) and enable pre-processing for always-on execution. A low-power or application-specific MEMS sensor hub can slash the number of CPU cycles that the classification engine needs. The onboard software can then directly generate fused sensor outputs at various sensor data rates to support efficient feature processing.Finally, retrain the model with system-supported data that can accurately identify the user’s activities.Additionally, cutting the data capture rate can reduce the computational and transmission resource requirements to a bare minimum. Typically, a 50 Hz sample rate is adequate for everyday human activities. This may soar, however, to 200 Hz for fast-moving sports. Reducing dynamic data rate selection and processing in this way lowers manufacturing costs while making the product lighter and/or more powerful for the consumer.High efficiency in processing AI data is key to fulfilling its potential, driving down costs and delivering the most value to consumers. MEMS sensors, in combination with sensor fusion and software partitioning, are critical to driving this efficiency. Operating at very low power, MEMS sensors simplify application development while accurately analyzing motion sensor data.Combining AI and MEMS sensors into a symbiotic system promises a new world of undreamt-of opportunities for designers and end users.This blog post is based on an original article that first ran in EDN. It appears here with the permission of the publisher.