Smart Data

Today, AI algorithms are primarily run at large data centers-- in the cloud. For this intelligence to be used at the edge, data must be transmitted to the cloud, analyzed there, and the results transmitted back to the field of operation, such as a mobile phone, medical implant or autonomous vehicle.

During the COVID-19 pandemic, the SEMI Global Advocacy team has been working tirelessly to ensure the microelectronics manufacturing and design supply chain is classified as an “essential business” in the United States and for similar designations in several other countries so that SEMI member companies can maintain operations. Their efforts have included direct lobbying and letters to the governors of 16 states in the U.S., 23 European countries and several European Union officials across the continent, as well as government officials in Japan, Mexico and Malaysia. The bedrock of these efforts, and the reason they have been highly effective, is that our industry enables both modern digital infrastructure and technology critical in the fight against the virus.SEMI takes immense pride in highlighting the role of our industry in providing the building blocks for innovations that improve social and economic prosperity the world over. It is never more apparent that necessity is the mother of invention than during a crisis, and the pandemic has created a diverse range of demands for technological advancements to address the myriad of challenges it presents. Our SEMI Tech Spotlight blog series highlights some of the many ways that our industry and member companies are enabling technology employed on the front lines of this fight – and that we strongly believe will ultimately help to win it. Our first piece in this series focuses on platforms enabled by big data and artificial intelligence.Fighting the Pandemic with Big Data-AI Enabled PlatformsThe COVID-19 pandemic is testing humanity in unprecedented ways, but it is also uniting us to fight this crisis with the best weapons we have. Big data and Artificial Intelligence (AI) technologies – built with microelectronic chips and systems that generate, transmit, store and analyze data – are making a profound contribution to our arsenal for this protracted war. Big data-AI technologies are enabling platforms such as data analytics, robotics, augmented/virtual reality (AR/VR), 3D printing, and others that are already being applied to address many facets of this crisis.Big Data and Analytics Inform Policy In the fight against COVID-19, data analytics platforms are being used first and foremost to slow the rapid spread and to inform policy decisions. This requires analysis of massive amounts of data about public health and travel, often using AI algorithms. The state of California, for example, is partnering with companies such as BlueDot, Esri and Facebook to build a software platform that uses smartphones and location intelligence to track people’s movement and predict hospital needs. Taiwan owes its considerable success in limiting the spread of the virus to the extensive use of big data analytics for identifying and tracking carriers. Google and Apple are driving a joint effort that connects Bluetooth with their popular iOS and Android platforms to trace contacts of infected people. India has developed Aarogya Setu, a mobile app based on Bluetooth and location-mapping platforms, designed to alert citizens if they have crossed paths with another app user who has tested positive for the virus. This app was launched in 11 languages, and despite being entirely voluntary, it was downloaded by 50 million people in 13 days, making it the world’s fastest-ever to reach that number. Such contact-tracing apps, now being rolled out in at least 26 countries, carry inherent privacy and security challenges due to the sensitive data they access. While mitigation strategies like strict data anonymity and opt-in protocols are being implemented, these will need to be refined over time.Robotics Protect Frontline SoldiersToday’s robust robotics platforms are enabled by huge amounts of data from sensors and guidance from predictive AI algorithms. These robots can learn on the job, adapt to the environment, and work safely with humans. In this pandemic, they are perfect for minimizing human interaction with infectious environments. Companies around the world such as Boston Dynamics, Akara Robotics, UBTECH Robotics and CloudMinds have already deployed robots on the front lines of this war to assess patient health, disinfect hospital surfaces, and help health workers with Personal Protective Equipment (PPE).Robot drones are also delivering blood and other lab samples. For example, WakeMed hospitals in North Carolina launched the first drone delivery program approved by the U.S. Federal Aviation Administration with Matternet drones operated by UPS; while Terra Drone from Japan executed similar tasks in the hard-hit Wuhan province of China.3D Printing Speeds ManufacturingBig data-AI technologies enable 3D printing platforms by providing accurate 3D models for optimized designs and defect-free manufacturing. Low-cost, fast-cycle-time 3D printing has helped to alleviate at least some of the medical equipment shortages. For example, the U.S. Food and Drug Administration (FDA) has approved the first 3D-printed “Stopgap Face Mask” for liquid barrier protection from the SARS-CoV-2 coronavirus for healthcare workers. The U.S. Veterans Health Administration has developed this in collaboration with America Makes using an open-source database – the 3D Print Exchange from the National Institutes of Health. In another example, Formlabs worked with Northwell Health, New York’s largest healthcare provider, and University of South Florida (USF) Health to develop and test a nasal swab prototype over just one weekend, and it is now producing up to 150,000 test swabs daily. Prisma Health in South Carolina received emergency FDA authorization for VESper, a 3D printed device that allows a single ventilator to support two patients, and possibly up to four.Telehealth Becomes a “New Normal”Telehealth is not a new concept but is much enhanced by today’s microelectronics platforms that can collect and transmit rich datasets with very low latency. Further, rapid data analysis is increasingly supported by AI systems. The requirement for social distancing makes telehealth a perfect solution for many healthcare consultations. U.S. government data indicates that the daily average of telehealth claims from private insurance for upper respiratory infections increased nearly 12 times over the previous month from March 14 to April 1. Similarly, Teladoc Health coordinated 100,000 patient “televisits” in the week of March 8 – a 50 percent spike over the previous week, taking pressure off the healthcare system. The next generation of telehealth is likely to use AR/VR platforms, which use even richer datasets and AI to improve the accuracy and predictive capability of their underlying models. Consequently, these platforms can provide more realistic experiences and improved outcomes. At least 11 states in the U.S. are already working with AR/VR companies such as XRHealth and AppliedVR for primary care and many medical specialties. Accelerating the Search for a Vaccine or TreatmentThe way out of this pandemic depends on swiftly finding a vaccine and a treatment, ideally by fast-tracking the traditionally slow drug development process. Big data-AI technologies are at the forefront of such efforts globally, often using the most powerful supercomputers available. For example, researchers at the University of California, San Diego (UCSD) are using the Frontera supercomputer to build a complete model of the SARS-CoV-2 coronavirus envelope – a formidable task, requiring analysis of data from 200 million atoms and interactions between them. Researchers at Argonne National Laboratory are combining AI with physics-based models to search for a molecule that might disrupt the activity of the virus, a precursor to finding a treatment. Also, several companies around the globe such as BenevolentAI (UK), Gero (Singapore), Innoplexus (Germany-India), and Insilico Medicine (US-Hong Kong) are using AI platforms to accelerate the search for a solution. ConclusionUltimately, the success of technology is not measured by the number of bits and bytes or by the speed of algorithms. It is measured by every janitor who did not have to clean a hazardous surface because a robot did, by every doctor and nurse protected by a 3D-printed mask, and by every person whose life may be saved by the accelerated discovery of a vaccine or treatment. Big data-AI technologies, and the platforms they enable, are just coming of age – they give us hope that as they evolve in the future, we can use them to build a more resilient society and economy.Note/Disclaimer: The examples cited above are purely for illustration – they are neither comprehensive, nor intended to endorse any particular product or solution.The SEMI Smart Data AI initiative helps members realize full value in the intelligent future enabled by Big Data and Artificial Intelligence – including the large revenue upside, and the transformational potential for operational and supply-chain efficiency. For more information on the initiative, contact Pushkar Apte at [email protected] Manocha is President and CEO of SEMI. Pushkar P. Apte, Ph.D., is the Strategic Technology Advisor for the Smart Data AI Initiative 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.