Big Data

For business leaders implementing AI projects, these hurdles can lead to frustration with poor results, excessive costs and delays, and even actual harm and lawsuits in extreme cases. Addressing these challenges is critical for realizing the full potential of AI.

Guido D’hert, High Tech and Semiconductor Industry Europe Lead at Accenture, spoke about challenges semiconductor companies face in meeting sustainability goals and how to apply analytics, new green technologies and circular design, sharing during his presentation at ISS Europe in Brussels, Belgium.

Demand for hi-tech manufactured goods is at an all-time high and is expected to grow significantly in our new digital age, COVID-19 economy. This is especially true for semiconductor chips. Chip manufacturers have been working to meet this demand by building new factories and by optimizing processes and equipment in existing fabs. While there is much media coverage about new factories planned by leading-edge chipmakers and government investments in the semiconductor sector, greenfield fabs entail significant capital expenditures and are sometimes fraught with complex political concerns. As a result, they can take several years to complete and reach their planned production capacity. Instead, the semiconductor industry needs to optimize existing factories in order to increase productivity and yield and meet growing demand by implementing smart manufacturing solutions. Smart manufacturing solutions will inherently reduce costs with more efficient and automated processes, and those savings can be reinvested for the next wave of solutions. Chip Industry on the Bleeding Edge Semiconductor manufacturers have always been focused on bleeding-edge technology to outflank strong competition and build the best products – faster and cheaper. Today, pioneering organizations are using data to optimize manufacturing processes and equipment, a practice known as Smart Manufacturing. While there are many definitions of Smart Manufacturing, the essence is maximizing the utility of big data generated in these factories by leveraging three pillars: Sensing, Connecting, and Predicting. It is not just the digitization in manufacturing, but it is also about turning the data into actions that generate value – an effort the SEMI Smart Manufacturing Committee is driving based on the three pillars. Optimizing return on investment is the ultimate goal. SEMI Smart Manufacturing Initiative activity is based on three pillars that support the goal of increasing ROI. Making the Right Decision, Faster Smart manufacturing practices enable organizations to make the right decisions and take action faster based on insights generated from real-time and historical data. This requires data management technologies and applications that can process, analyze, and act on information instantly. It has become ever more difficult to process and discern the relevant data or signal from the vast volume of data, perform analytics or develop new ML or AI analytic tools, and then make the critical decisions to solve problems as close to real-time as possible. Who’s Responsible – IT or OT? In the past IT (Information Technology) and OT (Operations Technology) were separate entities within organizations, with IT focused on storing large amounts of data for enterprise systems and OT concentrated on using data to perform specific functions. Smart Manufacturing often demands combining IT and OT, difficult in rigid organizations that operate the two organizations independently and lack the infrastructure to implement comprehensive solutions. Success requires executive leadership sponsorship, motivated technical personnel and, most importantly, a clear deliverable on the value in implementing Smart Manufacturing. Many organizations have introduced top-level leadership positions such as a Chief Information Officer or Chief Data and Analytics Officer to address this convergence and many of these leaders are embracing Smart Manufacturing practices. The SEMI Smart Manufacturing community includes many of these leaders and therefore has highlighted the importance in the return on investment for Smart Manufacturing solutions. Read more about IT and OT convergence and note that Smart Manufacturing is synonymous with Industry 4.0. The SEMI Smart Manufacturing Initiative covers the entire supply chain. Get Smart in Smart Manufacturing While new technologies and applications are being created to deal with mountains of data, it is the underlying methodologies and practices that are key to a successful Smart Manufacturing deployment. SEMI, the trade association representing the electronics manufacturing and design supply chain, is in a perfect position to evangelize Smart Manufacturing experiences and best practices for the entire manufacturing community. The more than 30 member companies participating in the SEMI Smart Manufacturing Initiative bring more than 500 years of collective experience and knowledge to the topic. Many segments of the supply chain participate in the SEMI Smart Manufacturing Initiative including packaging, assembly, SMT and PCB assembly, test, software, data management, sensor and material suppliers. Learn How to Manufacture Smarter SEMI SMART Manufacturing is hosting two great conferences in the coming months – the Global Smart Manufacturing Conference (GSMC) and the SEMICON West Smart Manufacturing Pavilion. As a leader of the organizing committee and chair for the SEMICON West Smart Manufacturing Pavilion, I encourage people who want to learn how to implement Smart Manufacturing or expand their knowledge of Smart Manufacturing to attend these events. The GSMC will feature keynotes highlighting the value of Smart Manufacturing, offer tutorials on the three pillars, and introduce several case studies for each of the pillars. Thirty-two organizations – ranging from global cloud providers, semiconductor factory operators, leading equipment vendors and software application solution companies – will present. See the full agenda here. The SEMICON West Smart Manufacturing Pavilion will compliment GSMC by showcasing a number of use cases that highlight the value of Smart Manufacturing. Panel discussions will deep dive into the challenges of implementing these best practices and the direction smart manufacturing is taking in the coming years. Our goal for these events is for you to take this knowledge back to your companies, implement and improve on the detailed solutions highlighted at the conferences, and return next year to share your success stories with the community. See you soon, in person or virtually! About the Author Bill Pierson is VP of Semiconductors and Manufacturing at KX, leading the growth of streaming data analytics in this vertical. Bill is also a chair for the SEMICON West Smart Manufacturing Conference and an active team member of the SEMI Americas Chapter. He has extensive experience in the semiconductor industry including previous experiences at Samsung, ASML and KLA. Bill specializes in applications, analytics, and control. He lives in Austin, Texas, and when not at work can be found on the rock-climbing cliffs or at his son’s soccer matches.

Ride the Wave of Smarter Manufacturing The year 2020 sparked a tremendous acceleration in the digital transformation worldwide, driving a sharp rise in demand for semiconductors and escalating pressure on chip factories to reduce manual functions on the shop floor. The mindset of the semiconductor industry saw a remarkable shift as it recognized with heightened urgency the need to deploy data-driven visualization, analysis, scheduling and dispatching solutions to increase automation to improve production speed and efficiency. Amidst the new excitement around Industry 4.0, chip manufacturers are rapidly deploying new technologies including IIoT, big data, machine learning and Autonomous Intelligent Vehicles (AIVs). Yet for many chip manufacturers, the path to building a smart factory is far from clear because they lack an overall digital transformation strategy. Smart manufacturing is a broad concept covering an array of technologies and solutions, making a holistic, mid- to long-term digitalization strategy rooted in the overall business strategy crucial. There are no shortcuts that can move a manufacturer instantly to Industry 4.0. Instead, this transformation is a step-by-step undertaking with a natural evolution. Some Factory Tasks Must Remain Manual – For Now The semiconductor industry has reached a point where manual processes are no longer efficient enough to support mass chip customization and remote operations. The many technological and standardization advances behind automation can help streamline some of a factory’s most labor-intensive tasks including the loading or unloading of machines or lot tracking and data collection while reducing operational costs. Still, some tasks remain very difficult to automate. For example, handling errors and exceptions presents the greatest challenge since some errors are hard to anticipate. What’s more, the cost of automating error handling can be prohibitive. Eliminating Gaps in Connectivity Often, critical data sources aren’t available due to lack of equipment integration, incomplete product quality monitoring or gaps in material tracking. Closing these gaps in connectivity enables the collection of data and provides rich, reliable information for analysis and reporting that can drive continuous operational improvements, optimizations and efficiencies throughout a factory. But keep in mind that data integration alone can be a challenging task. The selection and proper enrichment of relevant data is, in many cases, not just a technical problem but requires a detailed and in-depth knowledge of the manufacturing steps to be analyzed and optimized. Even when data is available, it might be still difficult to make decisions or implement improvements if it is in siloed systems that require manual processes to integrate and translate into useful information. Problem solving at this level is possible but extremely time-consuming. Manual integration is not only ineffective but costly, draining time, human resources and money from the factory. The right contextual information for the data is vital to unleash its potential and make improvements possible. Dispersed solutions cannot control processes because they span functional areas and people, physical and business entities. Backbone software for shop-floor operations that controls all other applications is central to smart manufacturing. Data-Driven Manufacturing The semiconductor industry is expert in data collection and leads many other industries in this area. The problem is often that chip companies use only a fraction of the information they collect for the analysis and insights needed to improve operational efficiency. By comprehensively integrating all distributed data into a single version of truth – in one location where it is always available – companies can make data analysis and problem solving almost frictionless. Keep in mind that data platforms and edge solutions, within the context of manufacturing, will not be adopted as part of a greenfield initiative. Building a solid automation architecture is only feasible and beneficial by deploying new technologies such as machine learning and artificial intelligence (AI). Analysis of historical data provides important context and reveals deviations such as unexpected process time, uncommon material accumulations or issues with material transport. By integrating swift control actions for new data point collected, manufacturing operations can shift from reactive problem-solving to proactive analysis and operational improvements. The tremendous increase in interest and investment in AI for manufacturing automation only became possible with the availability of low-cost sensors that generate huge volumes of data and solutions for storing and processing that at low cost. AI and other leading-edge technologies transform the tedious but critical process of extracting insights from data, making it instantaneous, streamlined and achievable for every manufacturer. The maturity of smart manufacturing hinges on the extent to which a factory is data-driven. This requires foundational investments to improve traceability, connectivity and real-time operations – and finally making sure that data helps us what to do and when to do it. Ricco WALTER is managing director of SYSTEMA Automation in Singapore.

The costs of production are typically based on labor and materials and define manufacturing expenses. But is this approach accurate enough? What about the cost of poor quality and lack of efficiency in production? How is the pandemic impacting semiconductor manufacturing and what can we expect from the future?SEMI recently spoke with Dr. Eyal Kaufman, founder and CEO of QualityLine, a Kiryat Gat, Israel-based provider of smart manufacturing analytics solution, about manufacturing controls and how to select the best data source to improve product quality and yield. Kaufmann provided a snapshot of current best practices used by the company to improve manufacturing efficiencies and product quality while reducing costs. He also discussed the COVID-19 pandemic’s impact on semiconductor smart manufacturing and how artificial intelligence (AI) can help keep factory workers safe.For additional insights on smart manufacturing, join the virtual SEMI Global Smart Manufacturing Conference, October 20 - 22, 2020. Registration is open.SEMI: Real manufacturing costs are calculated based on different aspects such as failures in production, repairs, products returned, scrap of components or late deliveries. Lack of quality and efficiency in manufacturing can undermine a business. How are you helping businesses overcome these challenges?Kaufman: To increase profit margins, it is essential to identify inefficiencies and what improvements to prioritize. Once manufacturing quality and efficiency deficiencies have been measured, the next step is to continuously collect manufacturing data in order to run the final cost analysis and use the analytics to improve the manufacturing process.Smart manufacturing makes it possible to detect anomalies in automated factories, improve production performance and increase profitability. Today, automated data are collected from every machine and piece of test equipment in the factory. Still, manufacturing data collection in many industries remains manual and expensive because of the time and human resources involved. A real-time analytics system can automatically collect all data sources and select the relevant data for analysis, which today is the most accurate and effective way of measuring and resolving quality and efficiency deficiencies.Data-driven decisions made by smart manufacturing reduce costs and improve manufacturing strategies, enabling factory operators to increase product quality, drive higher production capacity and enhance product design for manufacturability. Analytics solutions monitor shop floor operations accessing vendors and subcontractors’ products criterion to run root cause analysis. All those data will reduce the return rate of faulty products and accelerate return on investment. This is why we definitely need smart manufacturing technologies!SEMI: Data accumulated during the manufacturing process includes vital information about failures, anomalies and machine usability. What data are necessary to create the best analytics solution?Kaufman: Many companies today run data mapping and automatic creation of data capture. They often wonder if they need to use testing data, sensors data or product design data, or whether they should collect feedback from their customers and vendors. The best way to create an effective manufacturing analytics system is to use data sources such as: Feedback from customers (returned units, customers complaints, etc..) Testing data from automated test equipment and manual test activities Feedback from technicians repairing faulty units Analysis of testing processes done by vendors Sensors data Data from our ERP/MES systems Artificial intelligence enables any type and size of data structure, even accumulated data, to be automatically integrated and interpreted. AI-based analytics can also establish correlations between each manufacturing stage to help factory operators quickly conduct deep diagnostic and root cause analysis for problem solving and prevention – all while leaving intact a factory’s existing process, machinery and data output. Machine learning evaluates how a factory runs its database and puts all the information generated into an analytics solution that provides the know-how to continuously improve factory efficiency.SEMI: How do you select the best data source to improve manufacturing quality and yield? Kaufman: The accuracy and integrity of data accumulated in our manufacturing process is key to controlling and improving yield and quality while reducing manufacturing costs. Smart manufacturing is a technology-driven approach that uses digital and remote connected machinery to monitor the production process. The goal is to identify anomalies in manufacturing processes and leverage analytics to improve process yield and product quality.To select the relevant data, we collect each type and source of data that can improve the efficiency of a real manufacturing cell: Test data from Automated Testing Equipment Test data from Manual Testing Processes Analyses of repairing processes (failed units during the manufacturing process and units that were returned from customers) Once the data structure is collected, the next step is to turn it into actionable information in the manufacturing process. QualityLine smart manufacturing solutions provide a complete one-stop solution to interpret any manufacturing data structure. Our advanced manufacturing analytics solution detects quality and yield anomalies to reveal production line inefficiencies and opportunities to improve manufacturing quality and efficiency.SEMI: How would you describe your approach?Kaufman: Industry 4.0 in manufacturing claims to be the fourth generation of the industrial revolution. Advanced technologies like manufacturing intelligence and machine learning can efficiently achieve zero defects on manufacturing lines. Digital factories leverage technologies and methodologies including: Big data Self-optimization Self-configuration Self-diagnosis Cognitive and machine learning Smart manufacturing technologies enhance the manufacturing process by continuously collecting and analyzing data in real-time to achieve and maintain high quality performance. The goal is to achieve a significant increase in efficiency and yield while reducing waste and inefficiency.Until now, there has been no viable way to integrate all saved manufacturing data into a unified database. QualityLine advanced manufacturing analytics make it possible for any factory to become digital without installing new hardware, which can be expensive and require not only the extensive integration of existing data but investments in training. Our user-friendly solution integrates manufacturing data for industries with zero automation by first collecting and analyzing data from any type of manual test procedure and then integrated it into manufacturing analytics to improve efficiency.SEMI: Why are Pass/Fail criteria insufficient for controlling manufacturing yield and quality?Kaufman: Managing a mass manufacturing process is always a challenge because hundreds of tasks must be successfully completed before products can ship to customers. At QualityLine, we establish a test process for each stage of the production flow, from the incoming raw material to the final stage prior to the delivery of finished goods to the client. To prevent unexpected downtime incidents, waste and defective products, we collect and interpret every type of relevant data and turn it into meaningful information, setting up the following capabilities: Collection and interpretation of test and process data of each single unit and from each process and plant Automatic detection of quality and yield problems Accurate and quick root cause analysis process Automatic alerts to abnormal issues Prediction process potential and level of failures Measurement of key performance indicators Many manufacturers base their test criteria of each parameter on one key indicator – Pass or Fail. If the test result shows a Pass, then the unit is ready to move on to the next manufacturing stage. If the test result shows Fail, then the unit is sent to a technician for further analysis.A simple Pass or Fail criteria for product quality is far from sufficient since it provides little or no information about edge cases, where one or more of the technical parameters of the unit under test is only within its allowed tolerance. Edge cases may lead to unit failure during operation such as in extreme environments (cold, heat, humidity, electrical overload, impact, etc.). In fact, when running a mass manufacturing line, it is impossible to continuously digest all the detailed information collected from testing stations. Data is analyzed in detail only when a critical quality problem emerges and further analysis is required to understand the root cause.Information overload and the disregard of important parameters makes it hard to control the process and improve quality and yield. New technologies make fast and scalable data integration possible so data can be collected in real time to detect quality issues early, identify complex process disruptions to avoid delivery delays and ensure the best possible product for customers. Only by accurately analyzing data as actionable information can factory operators control the manufacturing quality process.SEMI: How has COVID-19 impacted the smart manufacturing market? How has your technology helped factories remain online?Kaufman: Smart manufacturing is playing a significant role by helping manufacturers overcome COVID-19 challenges such as workforce reductions, social distancing, drops in sales for some specific products and extreme pressure to cut operational costs.Manufacturing leaders turned to us for a solution to the challenges of maintaining efficient factory operations with a limited workforce and reduced number of operating hours. Filling factory orders with fewer people on the floor is a struggle. Digital factory technologies enable remote monitoring of operations to increase efficiency and capacity. We are helping our clients improve efficiency while reducing costs. Our remote monitoring technology can provide the operational visibility to floor managers and engineering teams who cannot go physically to the factories due to safety restrictions. With our advanced manufacturing analytics, they have full end-to-end visibility and can remotely diagnose and solve production line issues. During this critical time, we are proud to be improving remote monitoring solutions to help the industry withstand the pandemic. Some of our clients would have closed their factories otherwise. We’ve been working to integrate manufacturing data in factories that were previously unautomated to drive high automation levels. Integrating processes with existing factory data, regardless of customer’s protocols or automation level, is our great technology advantage.SEMI: How will manufacturing and its supply chains look after COVID-19?Kaufman: Smart manufacturing is currently a necessity. We collect and analyze data not only to improve quality but to reduce client returns of faulty products by 50% and reduce waste by 22%, both critical points. Manufacturing challenges will continue to accelerate advancements in technology and improve efficiency, safety and productivity as more factory operators incorporate real-time data analytics and artificial intelligence (AI). SEMI: Will suppliers continue to explore new avenues for smart manufacturing technologies and what are their growth opportunities?Kaufman: Yes, definitely. The sector has already changed, with COVID-19 bringing both opportunities and challenges. Industry leaders are facing new pressure, with sudden materials shortages, drops in demand and worker unavailability. The growth opportunities for manufacturing are likely to be digital, as already evident in the immediate response to the crisis. Industry 4.0 solutions will be crucial to increase end-to-end supply-chain transparency, automation and data integration. QualityLine manufacturing analytics have improved key manufacturing performance metrics. For example, based on customer feedback, we’ve increased production yield by 30%, saving some of our customers millions of dollars. Improvements like this can help suppliers withstand pandemics.Dr. Eyal Kaufman, Founder and CEO at QualityLine, has senior management experience and over 25 years of expertise in business development, marketing, finance, operations, engineering and quality management at leading industrial companies. Prior to QualityLine, he served as VP of Mobileye, Cardo Systems, and Medisim Ltd., as well as CEO of OnTheGo Systems. Eyal holds a Ph.D. from California Intercontinental University, an MBA from City University of New York and a BSc. from the Technion in Israel.The SEMI SMART Manufacturing Initiative is a global effort to promote awareness and interest about smart manufacturing with focus on delivering industry-recognized best-in-class programs and services to enable members to maximize product quality, productivity and cost improvements through smart manufacturing. Activities are focused on building out core capabilities to enable smart manufacturing across the microelectronics supply chain.MADEin4 is a consortium of 47 partners from 10 countries connecting the full range of supply chain: from semiconductor equipment manufacturers and system-integrating metrology companies to RTOS and key applications such as the automotive industry. The MADEin4 Project develops next generation metrology tools, machine learning methods and applications in support of Industry 4.0 high volume manufacturing in the semiconductor manufacturing industry.Serena Brischetto is a senior manager of marketing and communications at SEMI Europe.

MEMS and image sensors are shining stars in the chip industry as technology companies worldwide accelerate innovation in the fight against COVID-19. The tiny devices are behind advances in areas of electronics ranging from thermal imaging and faster point-of-care testing to microfluidics-based polymerase chain reaction (PCR) tools and techniques to detect SARS-CoV-2.SEMI recently spoke with Yole Développement analysts Dimitrios Damianos and Chenmeijing Liang about MEMS and imaging sensors market trends and how microelectronics-enhanced technologies are supporting the worldwide push to contain the spread of COVID-19.For additional insights on the technologies, join the SEMI MEMS Imaging Sensors Summit, held for the first time at SEMICON Europa, 12-13 November 2020 in Munich, Germany. Registration is open.SEMI: Despite the global pandemic, the MEMS and sensors market is still growing and is one of the healthiest industries, not only in Europe, but globally. What is driving this growth?Damianos: MEMS have been continuously evolving from the first sensors that were measuring pressure and acceleration to rotation sensing and visible light management followed by light sensing beyond visible and the expansion to ultrasound and multi-spectral. Now we are heading towards an era where we want to sense every aspect of our environment, with more processing and eventually analytics bringing more quality to the data.COVID-19 has impacted various global markets in very different ways. While automotive, mobility and civil aviation have suffered, the impact on telecommunications and medical has been positive. The effects on the consumer, mobile and industrial markets have been moderate. Moreover, COVID-19 is changing the perception of the current global supply chain in manufacturing, potentially leading to more localized value chains and further regionalization in order to minimize similar risks posed by the pandemic and the first lockdown.SEMI: Who are the main MEMS players based on your research? Damianos: For MEMS players, the picture in 2019 was not the same as 10 years ago, when Texas Instruments (TI) and Hewlett-Packard (HP) were leading the scene, with Bosch and ST Microelectronics following, all at comparable revenue levels. Now, Broadcom and Bosch lead with almost $1.4 billion in revenue each, and the rest of the MEMS key stakeholders compete in the $400 million to $600 million league. Microphone players profited from the voice interface adoption trend, while players active in MEMS for mobility and smartphones suffered slightly due to weak end-system demand.SEMI: What scenarios can we expect for each market with regard to the impact of COVID-19 on MEMS for 2020? Damianos: For 2020, at Yole Développement we expect the consumer market to contract slightly by 2.6%, with the automotive market to dip by 27.5%, and defense and aerospace by 20.5%. For the defense market, no major effect is expected, as all major programs still run for the year. The market may experience some slight delays in deliveries due to supply chain and logistics problems. However, sensors integrated in commercial/civil aerospace applications will suffer due to the general paralysis of the air travel industry. On the positive side, telecommunications could increase by 4.7%, medical applications by 10.6%, and industrial by 11.5%.Due to the global pandemic, some types of MEMS have spiked in demand this year. For example, demand for thermopiles and microbolometers used in temperature guns and thermal cameras has increased because of the need for contactless monitoring of people’s temperatures. Moreover, microfluidics for DNA sequencing and real-time polymerase chain reaction (PCR) diagnostic tests for detecting COVID-19 are gaining market relevance, with the latter serving as a premier method of detecting a bacteria or virus on the molecular level with high degrees of accuracy. Furthermore, pressure and flowmeters in ventilators will grow because of huge demand by hospital intensive care units (ICUs).SEMI: What growth trends do you predict for the long haul?Damianos: In the longer term, we expect global MEMS volumes to almost double, from 24.4 billion units in 2019 to 50.8 billion units in 2025, with a 13% CAGR during the same period. The global MEMS market could reach $17.7 billion in revenue by 2025.We see a trend to more wearable devices integrating a lot of sensors but also a move to a more consumer-oriented healthcare. Moreover, everything related to voice interfaces and voice/virtual-personal assistants (VPAs) will continue to see strong growth, increasing demand for MEMS mics with better quality and high-fidelity voice capture. MEMS devices are shifting to higher accuracy, ultra-low power, embedded intelligence and possibly some bio-compatibility for medical applications.MEMS players will try to escape the commoditization cycle and deliver more value by increasing the value of the data, either grouping many sensors to create sensor hubs or by adding processing, algorithms and software. Industry players are employing strategies such as adding extra processing close to the sensor (e.g. Knowles) or ameliorating the use cases of their applications of their clients (e.g. Bosch or ST). AI on the edge seems very alluring for extra value acquisition, with many startups already working on it. Some examples include always-on-sensing (Aspinity in collaboration with Infineon, Syntiant), echolocation (IMERAI) and predictive maintenance using inertial sensors (Cartesiam). This will be the next pit stop for MEMS technology for sure. SEMI: The CMOS Image Sensor (CIS) is a cornerstone technology in the development of devices powered by machine sensing and artificial intelligence (AI) for applications such as advanced driver assistance system (ADAS). CIS powers many of the ongoing revolutions in new technical products and use cases. What is the status of the image sensors industry? Liang: Last year was exceptional with a combination of high demand and high prices due to capacity limitations. Q4 2019 went way above the forecast, and, in the end, the CIS industry reached $19.3 billion for the full year. This year, we think it will return to normal, and, despite the pandemic impact, we expect significant growth in the range of 7% to 12%. Last year’s 25% year-over-year (YOY) growth was the highest we’ve seen over the past decade. Mobile still dominates the marketplace for CIS with 69% market share. Two markets, computing (8%) and consumer (5%), are adjacent to the mobile market but progressively losing ground due to the smartphone disruption.Security, at 6% market share, will probably be the second largest CIS market in the future. Although this is an area of excellence for the emerging Chinese players, unfortunately, they could be hit by the current trade war. The automotive market did very well from 2018 to 2019 because of the numerous applications recently developed for ADAS, viewing, and in-cabin applications. Lastly, the industrial camera applications benefited from large investments in automation, especially in the semiconductor and automotive industries, but here again many uncertainties remain as these markets will reshuffle in the post COVID-19 world. SEMI: Which CIS markets are most susceptible to seasonality and the impact of COVID-19?Liang: According to our quarterly CIS monitor, automotive and security were both negatively impacted by the pandemic beyond what we expected in terms of seasonality. For computing, the situation improved just prior the lockdown. Q1 got a positive impact with high sales results for laptops and tablets, but no significant impact was seen for security equipment. For automotive, the demand for cameras was very high in Q1, which is seasonally normal, despite the decrease of car shipments that followed later. The automotive CIS market in 2020 should remain relatively flat compared to 2019 due to the higher attachment rates of cameras despite the lower number of cars produced. Consumer and industrial segments dropped in Q1, which is typical early in the year.The next five years might be a bit slow, and although we forecast growth for the next year, in the future the market share will be lower in mobile. In fact, mobile CIS growth will fall below the CIS growth average, but we will see an increase of market share for the security, automotive and industrial segments. The CIS market could reach $28 billion in 2025.At first, COVID-19 had a limited impact on the production side, as factories in China are usually closed for the New Year holiday, when the pandemic started. While supply is currently recovering, we still consider the limited impact on demand. Smartphone production for 2020 will be down 6%, but camera shipments for mobile should increase about 10% this year. Another positive trend for the mobile market is optical fingerprint implementation. Currently, high-end Android phones use this kind of technology. For 2023, we estimate optical fingerprint technology revenue to be over $1 billion.The roadmap for the automotive market is driven by camera proliferation. We’ll see 10 cameras per car and more for some high-end vehicles. Increasing demand for safety and convenience will mean more cameras per car in the future. With a strong attachment rate, the market average in automotive is around 2.0 cameras per car nowadays, and we expect the market average to reach 3.5 cameras per car in 2025. In security, Charge Coupled Device (CCD)-based cameras are nearly out of the market, as CMOS-based IP cameras are most important now.SEMI: What are current key technology trends?Liang: 3D semiconductor technology is the hot topic. CIS wafer staking technology is indeed at the center of the CIS technology race. Future applications could be AI analytics or recently developed applications on new types of CIS. So far, we have seen the introduction of variants of the CIS pixel. Global shutter (GS) and indirect Time of Flight (iToF) were recently introduced, and now direct time-of-flight (dTOF) pixels are being used in high volume. 3D semiconductor technology is a bonanza for the industry, as it allows to pack more value in a single chip. While the surface of silicon is still increasing, additional silicon is added through stacking.With COVID-19 still a problem, the endpoint for smartphones in 2020 remains uncertain. The short-term impact for CIS will be slower growth with respect to the 25% YoY of last year. The downturn in car production will be mitigated by an increased attachment rate for automotive cameras. The security market will also help maintain CIS growth.For more insights, see the following reports: Status of the MEMS Industry 2020 3D Imaging and Sensing 2020 CIS Market Monitor Q2 2020 Dimitrios Damianos is a technology and market analysts at Yole Développement covering MEMS, Sensors, Photonics and Imaging. Chenmeijing Liang is a technology and market analysts at Yole Développement covering Imaging. Serena Brischetto is senior manager of Marketing and Communications at SEMI Europe.

Thanks to developments in science and technology, artificial intelligence (AI), cloud computing, big data and other technologies have been used to establish smart healthcare systems that helps societies respond more effectively to disease outbreaks. The spread of novel coronavirus starting in late 2019 has revealed how not only traditional medicine but also Smart MedTech applications can be instrumental on the anti-epidemic front lines.To give updates on the development of Smart MedTech and how it shines during the fight against COVID-19, SEMI invited Dr. Pei-Yuan Lee, Honorary Superintendent of Show Chwan Memorial Hospital, to share with MSIG (MEMS Sensors Industry Group) and Flex-Tech members how the international community and Taiwan are bringing their best in Smart MedTech to the table and how their collective efforts are helping tackle COVID-19 challenges.Taiwan’s COVID-19 rapid screening reagents and antibody testing help curb coronavirus transmissions Taiwan’s medical community has demonstrated its prowess in responding to the COVID-19 outbreak. Using its nucleic acid extraction reagent, Taiwan Advanced Nanotech Inc. tested 128 specimens from passengers aboard the SuperStar Aquarius cruise ship in only eight hours in early February. Taiwan’s leading research institute Academia Sinica successfully synthesized the first group of monoclonal antibodies capable of recognizing the new coronavirus protein on March 8, enabling testing to be completed in 15 minutes. The College of Medicine of National Taiwan University announced on March 27 that its 30-second screening device had helped identify asymptomatic carriers. The devices detect COVID-19 in people with no symptoms if they have pulmonary infiltration and edema. It took only 14 days for Academia Sinica to successfully synthesized the first group of monoclonal antibodies capable of recognizing the new coronavirus protein. On April 22, three biomedical companies in Taiwan launched a COVID-19 test that produces results from samples of patient mucus in less than 10 minutes to greatly enhance testing speed. Once the test method is approved by the Taiwan government, it will take Taiwan’s medical strategy against COVID-19 to the next level.Artificial Intelligence: the key to upgrading traditional healthcare practicesAI is a key enabler of the transition from traditional medical practice to Smart MedTech. To help fight the COVID-19 outbreak, a National Cheng Kung University medical team developed a 30-minute coronavirus testing procedure that uses AI to read pulmonary X-ray images and automate medical records. Taiwan AI Labs leveraged AI to simulate how drug molecules combine with viruses to reduce research time by three to four years. AI ​​diagnostic technology from the Alibaba DAMO Academy (Academy for Discovery, Adventure, Momentum and Outlook) and Alibaba Cloud interprets CT images of COVID-19 patients with 96 percent accuracy in 20 seconds. AI-powered algorithms improve diagnostic test accuracy, allowing clinicians to quickly analyze scans of pulmonary lesions and quantify the severity of lung damage.Startups have also joined the fight against COVID-19. Taiwan's Internet of Things (IoT) startup iWEECARE invented the world's smallest smart thermometer patch. Heroic-Faith Medical Science launched a device that uses IoT and AI to monitor lung sounds. With Smart MedTech expected to be fertile ground for future venture investments, enterprises must find their niches in establishing new technologies in a much more systemic way. Taiwan startup Health-Faith Medical Science developed a respiratory diagnostics device that uses IoT and AI technology to monitor chest sounds in real time. Anti-epidemic technology to help fulfill smart medtech vision Many AI and big data technologies previously deployed in hospitals and healthcare systems are helping regions around the world speed their pandemic response. The United States and China have started to develop facial mask recognition systems powered by AI, while a team in the Department of Bioinformatics and Medical Engineering at Asia University has devised a facial recognition system combining IoT and AI technology with infrared thermal imaging cameras. At Johns Hopkins University, the Center for Systems Science and Engineering is using AI to create big data models that track global cases, people and traffic flow, and other variables for real-time data analysis that enables epidemiologists to more accurately predict COVID-19 transmission paths. Graphen, Inc., a New York-based provider of next-generation AI platforms, launched the world's first AI COVID-19 genetic evolutionary path analysis systems to gauge the virus’s transmission route and accelerate pandemic response. Both the United States and China are also using robots and drones to improve epidemic research and patient treatment. For the first confirmed case in the United States, robots were used to assist with medical care. In China, robots facilitate deliveries of disinfectants to makeshift hospitals built to expand the nation’s capacity to treat COVID-19 patients. While Taiwan’s robots are traditionally used for hospitality, transportation and disinfection purposes, future robotics research and development will focus more on medical applications that shift more work from medical staff to technology. With abundant technological resources and expertise, Taiwan can join hands with the rest of the world to combat the COVID-19 pandemic. Emerging technologies are pointing the way toward a new paradigm for healthcare community. Biotech, artificial intelligence, and robotics have given rise to new applications that increase virus screening accuracy and efficiency. This growing wave of technological defenses against the pandemic will become a long-term force for stability and strength in healthcare systems across the world.To get involved in SEMI Taiwan Smart MedTech Community, please contact Helen Chen, Outreach Manager, at [email protected] Huang and Winnie Chang are marketing and public relations specialists at SEMI Taiwan.

This article is the fifth and final in a series highlighting the vital importance of SEMI Standards to commemorate the publication of the 1000th SEMI Standard in July 2019. Find the entire series here.As we define industry standards for managing data in the fab and beyond, we are creating a virtuous circle. More data create better processes. Better processes generate more good data, and more good data lead to better processes. It becomes a cycle of continuous improvement, and we are only just beginning to realize its potential. To dive deeper we interviewed Alan Weber, vice president, New Product Innovations at Cimetrix, and an active member of SEMI Standards Information and Control Committee (IC C).“Industry standards are critical in allowing us to collect information across the fab and use it in increasingly sophisticated control algorithms for the equipment,” said Weber. “The last few years have been about analysis applications that leverage big data in the fab. What started at the lot level is now applied at the wafer level, and for a process like lithography, it’s down to the shot or die level. We’re now collecting enough data variables at individual process and recipe steps to model for predictive maintenance and virtual metrology.”The migration from using data as rearview mirror for identifying and addressing fab issues to using data to head off issues preemptively represents a paradigm shift with immense advantages. This is the starting point for realizing a virtuous data circle.The benefits of a virtuous data circle are simple and compelling: higher yields, faster time to market, more revenue and greater profitability. Our optimism, however, is tempered by major obstacles to this promising future.Multilingual ManufacturingWeber points out that the electronics industry is becoming a multilingual standards world with more than 1,000 fab equipment vendors and several layers of protocols that present the challenge of seamlessly handling multiple protocols. His IC C Committee is out to tackle this challenge.“While SEMI Standards efforts first began in the front end, our standards program now encompasses the back end with test and packaging as well as other device areas including MEMS, sensors and displays,” said James Amano, senior director, International Standards and EHS, SEMI. “We’re going to see data connectivity from the front end to the back end to the final assembly of multi-chip products and that needs standards,” Weber explained. “We’ll need more connected equipment throughout the global, multi-site manufacturing process if we are to support the full traceability requirements of the most demanding markets such as automotive.”The industry will benefit from greater collaboration. Weber predicts that companies will team to create integrated supply chains within broader industry supply chains.Getting the Right People at the Right Time“As we lead the development cycle of a standard from concept to realization, one of the most important jobs of our standards task forces and committees is to coordinate competing companies and build an industry consensus,” Amano said. “This is the case for data in particular, where we rely on industry professionals like Weber and his colleagues, who are working to bring people together to collaborate on developing standards for connectivity and data sharing. It is that critical human element that allows SEMI to sustain our commitment to introducing standards that move the industry forward.”Will Companies Share Data If It Is Secure? Weber contends that when it comes to securing and sharing the data, the biggest challenge is to change the industry’s information-sharing culture.“Finance and defense are already finding ways to deal with data security,” said Weber. “While we will always have problems that require technology fixes, like dealing with new types of computer viruses, I am confident that we will be able to create standards that enable the free, secure flow of information. The key to making progress and better leveraging data is to get companies to see the potential of sharing data while investing in the standards.”SEMI recently launched a project to optimize data sharing across two critical process steps – lithography and plasma etch – to accelerate the adoption of data-driven AI methodologies. The results will help to establish data transfer and management standards crucial to the trusted exchange of trade secrets, IP and other sensitive information. Tools and materials from several SEMI members will be used for the project at Cornell University’s NanoScale Science Technology Facility (CNF). SEMI members are invited to join the project review team. Contact Pushkar Apte at SEMI ([email protected]) for more information on the initiative.Advantages Are Too Great to IgnoreTraditional cultural obstacles aside, the advantages of creating virtuous data circles are simply too great to ignore. Now that it’s accepted wisdom for fabs, factories and supply chains to continuously leverage interconnected data to get smarter, the time has come to extend those advantages throughout the full manufacturing process. Without these data circles, we’ll slow the development of new technologies and applications.We can only speculate where the lines of sharing data are drawn and will be redrawn in the future. But, without doubt, technology innovations such as AI will spawn new information business models that vertically and horizontally integrate companies in ways previously unimaginable. Data standards will underpin this structural transformation.Use your voice to affect standardization in and around the microelectronics industry. Learn about SEMI International Standards – and become part of the solution. Heidi Hoffman is senior director of technology communities marketing at SEMI. Hoffman and her team shine a spotlight on the work of the more than 20 technology communities under the SEMI electronics manufacturing supply chain collaboration platform. Actively engaging community members in marketing programs that showcase their unique value, Hoffman’s team helps companies to grow and prosper through the power of connection, collaboration and innovation.

Despite market saturation and stagnation saddling many business sectors, MEMS remains a shining star in the semiconductor industry. Opportunities in automotive, consumer electronics, mobile, medical are rising. What is supporting this industry growth? Who are the big players on the horizon?SEMI spoke with Dimitrios Damianos, Technology Market Analyst, Photonics, Sensing and Display division at Yole Développement, about MEMS market dynamics and future trends. Damianos shared his views ahead of his presentation at SEMI MEMS Imaging Sensors Summit, 25-27 September, 2019, at the WTC in Grenoble, France. Join us at the event to meet experts from Yole and many other key industry influencers. Registration is open.SEMI: MEMS and sensors is one of the healthiest industries not only in Europe but globally. Despite a global economic slowdown, the MEMS and sensors is still growing. What is fueling this growth?Damianos: The value of the global MEMS and sensor market will almost double from $48 billion in 2018 to $93 billion in 2024. In 2018 the MEMS and sensor market represented more than 10% of the total IC market, as more and more MEMS devices and sensors, such as MEMS, image sensors, and RF filters, are integrated in end products in consumer and automotive. In particular, the value of the MEMS-only market reached $11.6 billion in 2018, with consumer applications accounting for more than 60% of the total market. From 2019 to 2024 the MEMS market will grow 8.3% annually in value driven by pressure (for TPMS), RF (for V2X 5G communications), inertial (for ADAS) and future MEMS (such as pMUT for ultrasonic fingerprint) (Source: Status of the MEMS Industry report, Yole Développement, 2019). SEMI: How are MEMS shaping the semiconductor industry today? Damianos: MEMS have a make-smarter enabling capability. They are providing context for new applications and services in transportation, mobility, health, and security. Large companies such as Alibaba and Google are considering MEMS as a critical element in their business solution domains covering the upcoming smart home, smart campus, smart city and smart industry applications. MEMS have key features that correspond to these companies’ criteria for accuracy, small size (without performance degradation), low power and always on (e.g. microphones). Furthermore, with the advent of sensor fusion and edge computing, more sensor data can be processed, maximizing the qualitative and useful information about us and our surroundings. This has a huge impact in all markets, especially consumer.SEMI: MEMS foundries performed well thanks to the boom in industrial and medical applications. Who are the big players right now?Damianos: During 2018, all foundries saw their revenue increase. STMicroelectronics, Teledyne Dalsa, Silex, IMT, Micralyne and Philips Innovation Service are important MEMS foundry players that offer services for various MEMS devices used in medical and industrial markets, among others. On one hand, medical applications were driven mostly by microfluidics, flowmeters, pressure and inertial MEMS. On the other hand, industrial applications were driven by inkjet heads, microbolometers and pressure MEMS. The market prospect, however, is huge for RF MEMS and oscillators that will be used in next-generation 5G infrastructure. SEMI: What is the current status of MEMS for automotive applications? What are the related market drivers? Damianos: In automotive applications, accelerometers and pressure sensors still account for the lion’s share in units. Pressure sensors will grow at more than 8% with Tire Pressure Monitoring System (TPMS) implemented in Chinese vehicles in the near future. After 2019 and 2020, with the new Chinese standard, GB 2614, TPMS will become compulsory: 100% of all new vehicles will have TPMS. Also, automotive MEMS could grow quicker than the corresponding car market (currently at approximately 3%). The reason is a higher number of many different MEMS devices that are being integrated in cars, such as MEMS inertial measurement units (IMUs), TPMS, environmental MEMS for gas and particle monitoring in-cabin and microphones for hands-free voice commands.SEMI: After years of decline, the inkjet heads industry is growing again. What other segments are benefiting from MEMS technology applications? Can you name two examples?Damianos: RF MEMS (BAW filters) is also benefiting from applications in smartphones and will continue to benefit with the arrival of 5G. 5G means additional high frequency sub-6 GHz bands that can only be addressed by BAW filters. Moreover, new infrastructure approach using active antennas will create an expanding market for BAW.Another segment is inertial sensors. Inertial MEMS already have a high potential in wellness and fitness wearables and are gaining support for medical wearable applications to monitor patient activity, with the aim to prevent seizure in cases of epilepsy and other mental disorders. Compared to other types of sensors, MEMS is the golden technology for inertial sensors integrated into medical wearables. They are used for rehabilitation systems, activity trackers and assistance living/fall detection. Specifically, the IMU market will continue to grow for consumer and automotive applications as their price and form factor continue to shrink and they replace traditional standalone MEMS accelerometers and gyroscopes. However, the inertial sensor market will mostly grow for smartphone applications (mostly 6DOF, with 9DOF volumes being comparatively low).SEMI: Give us one prediction about the opportunities offered by the MEMS technology. Damianos: Sensor fusion is becoming more and more relevant since billions of MEMS sensors are made every year. The upcoming 5G revolution will make connectivity easier than ever, creating exponentially more data. To make these data meaningful, data processing is mandatory. Big data is an industry born of recent advancements in AI and machine learning, built upon and fueled by a wealth of new data from ever-expanding sensor applications. An upcoming trend is edge computing, with sensors and MEMS driving a new age of technology. Sensors are digitizing the human experience, and as the real and virtual worlds move closer together, it will be sensors that bind them, enabling new experiences for users everywhere. Running AI at the edge, coupled with sensor fusion, will open new applications for MEMS in audio, motion, olfactometry, and imaging. We also expect that new MEMS devices (microspeakers, ultrasonic fingerprint, pMUT) and piezoelectric MEMS technology could rejuvenate the MEMS market. SEMI: What are your expectations for SEMI MEMS Imaging Sensors Summit and why would you invite your peers to attend? Damianos: SEMI is organizing another very successful event, gathering experts from the Imaging and MEMS industries. We are at a turning point of innovation, with many technological advancements in AI, IoT, AR/VR, biometrics, and other areas where Imaging and MEMS technologies are paramount. Yole is excited to hear the thoughts of many high-profile experts on existing activities and future prospects within their organizations. If you are too, then it is an event that you shouldn’t miss!Dimitrios Damianos, Ph.D. is a Technology and Market Analyst in the Photonics, Sensing and Display division at Yole Développement (Yole). Damianos is a member of a Yole team that produces technology and market reports on the imaging industry including photonics and sensors. Damianos holds a MSc degree in Photonics from the University of Patras (Greece). After his research on theoretical and experimental quantum optics and laser light generation, Dimitrios pursued a Ph.D. in optical and electrical characterization of dielectric materials on silicon with applications in photovoltaics and image sensors, as well as SOI for microelectronics at Grenoble’s university (France). He has also authored and co-authored several scientific papers in international peer-reviewed journals. Learn more! Join the webinar on 5th September 2019. Registration is open! Serena Brischetto is a marketing and communications manager at SEMI Europe.