machine learning

Over the past 50 years, the field of engineering simulation has developed numerical methods that enable engineers to solve 3D physics problems faster, easier, with greater accuracy and more robust results. Finite element analysis (FEA), finite volume methods (FVM) and finite different time domain (FDTD) have increased solver efficiency while dynamic visualization techniques improve what is often called user-friendliness. Despite these improvements, certain challenges still remain. Specifically, simulation requires the simultaneous trade-off of: Accuracy of results Speed of results Ease of use of the workflow Robustness of the workflow Take, for example, mesh generation, the building block of multiphysics solutions. It is well known that using coarser meshes increases simulation speed but will result in loss of accuracy. Similarly, easy-to-use workflows with simpler meshes also reduce accuracy, and can introduce other issues: The simulation may not converge and the robustness fails. Ansys is exploring the use of AI/ML to solve all of these problems. Simultaneous Improvements Commercialization of AI began in the 1970s, but the field actually got its start a decade earlier with the development of rules-based expert systems. The simplest form of AI, these systems rely on curated human expertise to solve problems that would normally require human intelligence. We’d expect that AI/ML applications would be actively used in science and medicine, from streamlining drug discovery and advancing robot-assisted surgery to automating medical records that can be instantaneously accessed by providers anywhere in the world. But AI/ML is rapidly being successfully adopted by an increasingly broad range of industries and users. It’s helping consumer brands mine their social media to find out customers’ feel about their products (sentiment analysis), giving investors a leg up on stock trade opportunities (financial algorithmic trading) and enabling e-commerce owners to personalize offerings to online shoppers (recommendation engines). At Ansys, we can use AI/ML methods to automatically find the parameters of simulation to simultaneously improve speed and accuracy. We believe applying AI/ML will enable us to: Further improve customer productivity Augment simulation, including accelerating chip thermal solutions and developing a fluids solver that combines high-fidelity solutions in local regions with ML methods in coarse regions Optimize design space exploration Drive business-intelligence decisions such as resource-prediction needs for our solvers Combine data analytics-based and simulation-based digital twins to create accurate and fast digital twin hybrids In other words, we believe that AI/ML will help us narrow the gap between the ideal world, where time, effort, efficiency and results are perfectly balanced, and what happens in real life – and make productivity, ease of use, and accuracy a little less of a trade-off. To learn more about applying AI/ML to autonomy, click here. Prith Banerjee is Chief Technical Officer at Ansys, Inc.

While flying cars have been a science fiction mainstay for decades, new sensors, software and other technology put personal air travel vehicles within reach. MEMS and Sensors Industry Group (MSIG) interviewed Dr. Alberto Speranzon, Fellow at Honeywell Aerospace Advanced Technology, about his upcoming keynote Sensors and Software Enabling Autonomy for Urban Air Mobility at the MEMS and Sensors Technical Congress (MSTC 2021) virtual event, April 13-15. Dr. Speranzon will discuss Honeywell’s challenges in enabling air taxis and the path forward to building out the necessary infrastructure. SEMI: People have dreamed of flying cars for decades. What has changed recently that makes them a near-term reality? Speranzon: There certainly are multiple factors that have contributed to pushing both new startups and established aerospace companies to make this dream a reality. Advances in battery technology are bringing electric aviation closer to being viable. There is still more to be done to achieve higher energy density in batteries, but already with today’s technology we can have vehicles that can fly from the suburbs to the downtown of a large U.S. city. At the same time, urbanization has created a lot of congestion, so finding new, efficient ways to move people and goods across megacities is becoming a critical need. Undeniably, the autonomous car industry has contributed to demonstrating that it is possible to achieve levels of automation and autonomy that were unimaginable just a few decades ago. The advances in sensing and computation required to make self-driving cars a reality is certainly going to help the aviation industry to develop autonomy in the air. SEMI: An air taxi must be highly sensorized. What types of sensors pose your biggest development challenge? Speranzon: Aircraft based on today’s battery systems simply cannot accommodate the size, power requirements and weight of sensors used by standard airliners. So there still is work to be done to reduce the SWaP (Size, Weight and Power) of these systems. Honeywell, for example, has developed a multipurpose radar, the IntuVue RDR-84K, that is only about the size of a paperback book. It can detect traffic, terrain and even weather, and was specially designed for air taxis and cargo UAS (unmanned aerial systems). Today, however, we still rely on human pilots to make the very complex decisions and, despite all the limitations that human eyes have, we do rely on them in a multitude of complex situations. There is also growing interest in integrating cameras into autonomous air taxis and similar platforms. Cameras can’t work alone, because they are affected by foggy, rainy and dim conditions. But they are lightweight, inexpensive, and require little power. That can make them very useful when combined with a compatible radar system like the RDR-84K. While these sensors bring new opportunities to the aerospace industry, they also pose some big challenges. For example, today’s state-of-the-art algorithms for image processing are machine learning algorithms called deep neural networks. They’re capable of extracting high-level information from pixels. But when it comes to aircraft certification, these algorithms face major hurdles. There is no software of this kind in any certified air vehicle, and it is unclear how regulators would certify neural network-based software components. Developers could avoid these new machine-learning algorithms and use standard computer vision methods instead. But they still face the challenge of deciding the type and quantities of images sufficient to declare the software “bug-free.” A similar set of questions will be also be true for radars, as they will be used to feed data more directly into the autonomy modules of future air taxis. So in the short term, we need to tackle the challenge of reducing the size and weight of sensors. But in parallel, we need to develop new ways to take advantage of machine learning: utilizing cameras and radars for autonomous decision making while still ensuring the highest standards of safety. SEMI: How is autonomous air mobility more or less challenging than autonomous ground vehicles? Speranzon: They are both challenging in their own ways. Autonomy on the ground – and I am thinking specifically of autonomous cars – is challenging as their “normal” behavior is very complex. We humans can drive from point A to point B over the public road network without a second thought. But to a machine, the heterogeneity of the people driving on the road, their sometimes unpredictable behavior, the changing weather conditions and shifting environments pose huge challenges. These things make what we call “normal driving” a very difficult problem to solve. At the same time, however, “off-nominal” scenarios in ground autonomy, while complex, are not orders of magnitude more complex than “nominal” scenarios. Ground vehicles can brake and stop, change lanes or move to the side of the road to avoid a crash or manage a malfunction. For autonomous air vehicles, the difference between nominal and off-nominal scenarios is more extreme. “Nominal” flying can rely on some of the existing aviation infrastructure, like communication between air traffic control and other aircraft. Air taxis can follow predefined paths and long-established aviation procedures as they move from vertiport A to vertiport B. This results in more automation than autonomy: everything is prescribed in advance and the onboard computer will follow what is pre-defined. Thus, nominal conditions will be fairly simple. However, in case of accidents or emergencies, aircraft face situations that are orders of magnitude more complex than nominal scenarios. An air vehicle cannot easily just “stop.” It could be 1,000-2,000 ft above ground, possibly above a bustling city. Human pilots go through rigorous training to be able to deal with emergencies like these. Consider the split-second judgments and airmanship behind the 2009 “miracle on the Hudson” landing. Asking autonomy to make the right decisions and execute emergency behaviors is a huge challenge. And these systems will need to be certified to the aviation industry’s very high standards. At present, we do not even have a well-established set of certification rules that an autonomous flying vehicle should comply with. SEMI: How soon might I be able to take an air taxi ride? Speranzon: Initial deployment of air taxis will happen around 2025. They will have human pilots but will use simpler interfaces than today’s cockpits. This first step will provide technologies that make it easier to take off and land, and to avoid traffic. That will reduce the need for highly experienced pilots and should help alleviate the overall shortage of pilots in the aviation industry. Fully autonomous air taxis are likely not going to show up until after 2030. In the beginning, they will likely fly only in regions where the weather is good most of the time. The autonomous car industry has already adopted this strategy, mostly deploying their technology in regions where the weather is dry and sunny. Soon, however, we’ll start seeing operations in “all-weather” scenarios and an increasing number of air vehicles within the same airspace. There is one critical stepping stone on the way to fully autonomous passenger aircraft: the success of fully autonomous cargo drones. For light parcels we will see initial deployments in 2022 or 2023, followed by larger UAS capable of transporting heaver cargo in 2024-2025. But whatever the timing, these are very exciting times. The aviation industry is witnessing a revolution with new vehicle manufacturers, new technologies and, likely, new applications we have not even dreamt of yet. Learn more about Honeywell’s work in urban air mobility and unmanned aircraft at aerospace.honeywell.com/uam. Alberto Speranzon is a Fellow within Honeywell Aerospace Advanced Technology. He received a Ph.D. in Electrical Engineering from the Royal Institute of Technology (KTH), Sweden in 2006. Since joining Honeywell, Alberto has been working on various aspects of autonomous systems for urban air mobility, leading such research areas as program manager and principal investigator. He is an IEEE Senior Member and a member of the Board of Governors of the IEEE Control Systems Society. Nishita Rao is product marketing manager at SEMI.

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.

SEMI spoke with Eyal Shekel, senior vice president of Service Strategy and Excellence at Tokyo Electron Limited, about the impact of artificial intelligence (AI) on smart manufacturing and how other fab solutions for smarter process tools are advancing semiconductor manufacturing.Eyal shared his views ahead of his presentation at the SEMI Fab Management Forum, 17 February, as part of the SEMI Technology Unites Global Summit, 15-19 February 2021, an online event. Join us to meet experts from Tokyo Electron and other key industry influencers. Registration is open. SEMI: AI technology is considered a key enabler for smart manufacturing. What are the latest trends? Shekel: The advent of advanced nodes and extreme complex 3D semiconductor geometry has lengthened time to market and increased costs in areas ranging from equipment development and large-scale metrology usage to monitoring yield inhibitors.AI is becoming a critical tool in the area of material informatics to determine suitable materials and processing techniques in order to meet the needs of future devices. Together with new materials and processes, the development and implementation of virtual metrology will enable accurate and almost absolute real-time monitoring of our customers’ device wafers at each stage of the manufacturing process.SEMI: What are the benefits of data analysis in the process from R D and Ramp-Up to High-Volume Manufacturing? Shekel: The new research field of materials informatics enabled by AI provides tools to guide the highly efficient discovery and optimization of production processes. For example, TEL has developed methodologies for co-optimizing processes and materials for etch rates.To monitor and manage the yield of semiconductor fabrication processes, direct metrology measurements are important. However, it is difficult to monitor all production wafers due to the time and cost involved. With deep learning AI, it is now becoming possible to predict every wafer’s metrology measurements based on production equipment data and previously processed wafer metrology variables. This enables total quality management and run-to-run control, while simultaneously reducing production costs and cycle time.SEMI: Can you tell us more about TEL Service Advantage?Shekel: TEL Service Advantage is a TEL global support organization that allows customers to select a service plan that fits their needs. Through TEL Service Advantage, we can quickly respond to customer requests and technical advancements. TEL Service Advantage provides various plans to maximize equipment maintenance efficiency for customers and productivity from equipment manufactured by TEL. TEL Service Advantage plans can be combined to meet customer needs and achieve maximum results.A key enabling element of TEL Service Advantage is TELeMetrics™. TEL analyzes equipment data from various sensors using a remote connection and, based on that analysis, provides solutions to customer-specific problems around equipment throughput and predictive maintenance.SEMI: How is AI helping during the pandemic? Can you share a success story? Shekel: The pandemic forced severe travel restrictions worldwide, making it very difficult or even impossible in many cases to visit our customers, as it is still the case today. Standard communication devices like smartphones and email helped at the beginning when TEL intensified the remote support by our Total Support Centre (TSC).TEL continued to develop its Service Advantage program quickly, and started using additional advanced tools and methodologies such as the following: Deployed AR (Augmented Reality) to remotely assist our customer and TEL engineers Secured remote connections into TEL tools to investigate parameters and logs, or to change set-up Used remote training courses that connects trainers via video conferencing systems and training tools in the factories to skill up engineers located in a different parts of the world Used AR glasses for tool start-up and troubleshooting Expanded TEL database global technology with multi-tool on languages search capabilities A key project at a customer site in Europe offers an excellent success story. Using all the approaches above, we collaborated with the local team to put a tool into production with no major delays. This was highly appreciated by the customer and very important for us.SEMI: What do you predict for the future? Shekel: Global technology infrastructure continues to develop and expand rapidly. Elements like 5G networks, IoT and advanced sensing capabilities will lead to what we call General AI, which will be based on neuro-like infrastructure. The auto learning will spread across domains and rely on internal logic and reasoning to automate many tasks that are manual today. In our industry in particular, General AI will enable workers to focus more on data analytics and future advanced R D rather than ongoing operations.SEMI: How can technology unite us? What do you expect from your participation at SEMI Technology Unites Global Summit?Shekel: Technology united us in the last 150 years. The connectivity started with telegraph and telephone and was used to exchange information over wider distances. Nowadays, video conference capabilities, AR and improving communications technology makes it much easier to unite people who are geographically dispersed. This becomes obvious and valuable especially during this pandemic period. As a fact, we are able to continue to perform all our key activities – our tool support, training and customer relationships – even if we cannot be present in person.The SEMI Technology Unites Global Summit is a great chance to stay connected to people and customers that I would normally meet at the SEMICON exhibitions.It also offers the opportunity to network with many more people who I would not be able to meet otherwise. Moreover, I can watch speeches and presentations at any time! Normally I would miss some programs since exhibitions and events took place at the same time.Eyal Shekel, senior vice president of Service Strategy and Excellence at Tokyo Electron Europe Limited, is a 27-year semiconductor industry veteran. Upon his graduation as a Mechanical Engineer from the Technion (Israel leading technical institute), he joined Applied Materials. In 1997 he moved on to Tokyo Electron (TEL) in Europe, served as the Regional Service Manager of Israel and, soon after, was appointed the company’s General Manager. Since 2005 Eyal has been part of TEL Europe senior management. He oversaw the Service and Support Operations for TEL Europe as a senior vice president until 2019. In his current role, he co-leads TEL’s Global Service Committee in Japan.The SEMI SMART Manufacturing Initiative is a global effort to promote awareness of and interest in smart manufacturing with a focus on delivering industry-recognized best-in-class programs and services to enable members to maximize product quality and productivity while reducing costs. 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 senior manager of Marketing and Communications at SEMI Europe.

SEMI spoke with Tom Doyle, founder and CEO of Aspinity, about the challenges of packing more localized intelligence into portable Internet of Things (IoT) devices without draining their batteries. Doyle shared his views on Aspinity’s system-level approach – solve the power problems by performing machine learning in analog – ahead of his presentation at the SEMI MEMS Imaging Sensors Technology Showcase, 18 February, as part of the SEMI Technology Unites Global Summit, 15-19 February 2021, online event. Join us to meet experts from Aspinity and other key industry influencers. Registration is open. SEMI: Why is power efficiency so important for IoT devices? Doyle: Hundreds of millions of IoT devices are improving our lives at home and at work. Always on and always sensing the environment for data, these smart devices have traditionally been wall-powered and have relied on the cloud for their data processing needs, but clogged networks, as well as privacy and performance issues, have necessitated the migration to edge processing.Spanning consumer, medical and industrial, these IoT devices are becoming smaller and more portable. And a portion of them is operating remotely in hard-to-access locations. So now we are packing more functionality into the device and we are moving to battery power and the batteries need to last a long time. That is a big challenge before us, and to answer it, we need to find the most power-efficient ways to integrate always-on sensing capability into IoT devices because we cannot afford to have short battery life limit market adoption.SEMI: Why is it so challenging to deliver low-power, always-on solutions and how can sensors suppliers achieve improvements in system power? Doyle: In today’s always-on IoT devices, all sensor data – which are naturally analog – is immediately digitized at high resolution, and then it’s analyzed to determine whether a wake word has been spoken, a specific motion has been made, or some other anomaly has occurred. But since most of the data collected will not contain the information for which the device is waiting, this digitize-first approach wastes significant battery life by continuously running irrelevant data through the ADC and the digital processor.Sensors suppliers have some options to consider for reducing power. If they are satisfied achieving incremental improvements in battery life, both sensors and digital processor suppliers can continue to drive down the power of each individual component in the system. But to achieve revolutionary power savings, we must look at a more holistic system solution.The fundamental problem is that moving data through a system costs power. That is why the most efficient way to save power is to reduce the amount of data down to what’s actually important as early as possible, right at the start of the signal chain, where the physical world becomes data. If we can minimize the amount of data that require downstream processing, then we can maximize battery life.SEMI: Aspinity aims to solve the battery-life problem in IoT devices by introducing a new system architecture. Could you explain how your approach differs from digitize-first?Doyle: Aspinity’s solution, called the Reconfigurable Analog Modular Processor (RAMP), is an analog processing technology that combines analog machine learning (analogML™) and analog compression to enable accurate, ultra-low-power analog event detection and system wake-up. RAMP technology enables a new system architecture, which we call analyze-first, that allows an always-on system to spend just a little bit of analog power up front at the sensor to determine whether sensed data are relevant to the task at hand before waking the digital system for further processing. The analyze-first architecture can extend battery life by months or years over digitize-first architectures because it keeps the higher-power digital components asleep unless important data require digitization and analysis, which in some applications – such as voice-first or acoustic event detection – may occur very rarely. Aspinity RAMP voice activity detection with preroll from Aspinity on Vimeo. SEMI: Can you give us an example?Doyle: Here is a practical example of how this works: For most voice-enabled systems, such as smart speakers, voice-activated TV remotes and hearables, voice is only present 10%-20% of the time – but the digitize-first architecture on which these devices are traditionally based is digitizing 100% of the sound data captured by the microphone, even when most of that data are irrelevant and could not possibly contain a wake word.In contrast, the RAMP-based analyze-first architecture is highly efficient since it uses feature extraction and a neural network to analyze the sound at the microphone, right where it enters the device, to determine if the sound contains voice before waking the digital wake word engine. Additionally, the accuracy of most wake word engines relies not just on waking up and analyzing the wake word, but also on analyzing the 500ms of sound prior to the wake word (preroll). To support wake word engine performance, the RAMP also continuously compresses 500ms of preroll that can be stored in just 2k of memory and delivered to the wake word engine along with the voice data. So, this new analyze-first approach using RAMP technology can extend battery life by 10 times over older digitize-first designs, without sacrificing performance and accuracy.SEMI: What solutions can Aspinity bring to address the current market needs? Doyle: Aspinity offers the only analogML chip for always-on IoT devices that run on battery: the RAMP chip.The RAMP is trainable and programmable to detect many different types of sensor events directly from the raw analog sensor data. One application that benefits from a RAMP chip are devices that are always-listening for voice, for glass break or alarms, or for some other type of sound. Other examples include vibration sensors that monitor industrial equipment for predictive and preventative maintenance, and heartrate sensors that are used to detect anomalies in wearables and other biomedical applications.Aspinity just recently introduced our voice-first evaluation kit – which we will be demonstrating during the Technology Showcase at Technology Unites – to enable our customers to get first-hand experience with our RAMP-based analog voice wake-up solution. With this complete hardware and software kit, customers can experience all of the benefits of analogML and analog data compression – 10x power savings without a reduction in wake word detection accuracy –for their next generation of voice-enabled devices.SEMI: How can technology unite us? What do you expect from your participation at SEMI Technology Unites Global Summit?Doyle: I think this past year has shown us that when time gets tough – and for many of us, the COVID-19 pandemic has been one of the most difficult challenges we have faced – that innovation is critical to solving major problems. The microelectronics industry has played an important role in providing critical components for COVID-19 testing, ventilators, air-purification systems, and other equipment used in healthcare settings. COVID-19 has also accelerated the move to voice as a preferred interface to many devices in an effort to stem the spread of germs on surfaces.The biotech industry is gearing up to provide the vaccines that we hope will restore more normalcy to our daily lives. We can thank the successful collaborations between R D innovators and established companies in many different markets for the new devices and drugs now going into production.With traditional in-person conferences still on hold until the pandemic eases up, attending industry conferences with exceptional speakers presenting interesting content is more important than ever. SEMI Technology Unites Global Summit provides that opportunity, and I’m genuinely looking forward to participating.Tom Doyle, Founder and CEO of Aspinity, brings over 30 years of experience in operational excellence and executive leadership in analog and mixed-signal semiconductor technology to Aspinity. Prior to Aspinity, Tom was group director of Cadence Design Systems’ analog and mixed-signal IC business unit, where he managed the deployment of the company’s technology to the world’s foremost semiconductor companies. Previously, Tom was founder and president of the analog/mixed-signal software firm, Paragon IC solutions, where he was responsible for all operational facets of the company including sales and marketing, global partners/distributors, and engineering teams in the US and Asia. Tom holds a B.S. in Electrical Engineering from West Virginia University and an MBA from California State University, Long Beach.Serena Brischetto is senior manager of Marketing and Communications at SEMI Europe.

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.

The seemingly simple act of commanding consumer devices by voice is a choice that nearly 118 million Americans now make every day, according to a recent report from eMarketer, the digital marketing research firm.While the voice interface is convenient for users, its implementation comes at the potential loss of individual privacy. The reason? Always-on, always-connected voice-first devices such as Amazon Alexa and Google Home require a wall plug and an internet connection to powerful cloud processors, making it possible for cloud companies — however benignly — to collect data on personal habits, location and conversation that were never intended for sharing. Move processing to the edgeTo address concerns over user privacy, device designers are attempting to do more of the audio processing within the consumer device, rather than sending users’ voices into the cloud. Moving more processing to the edge is a trend across the Internet of Things (IoT) industry, and not just for voice data but for other types of sensitive or proprietary data as well.Yet designers have realized limited success because the conventional approach to always-listening edge processing is notoriously inefficient: It digitizes and processes 100% of incoming sound data even though up to 90% of the data is irrelevant noise. This digitize-first approach wastes vast amounts of system power digitizing and analyzing the audio signal as it searches for a wake word when there isn’t even speech present, making it impractical for use in small, battery-operated devices.Workarounds don’t workTackling this power issue is critical to keeping private data secure. Unfortunately, it’s also exceptionally difficult. Design engineers have tried workarounds to decrease power consumption in an always-listening system, including duty cycling and reducing the power of each individual component in the audio signal chain that handles the data. The reality is that these kinds of approaches don’t address the root cause of the problem: too much data.To truly tackle the problem, we need to change our approach to a system solution, not a component solution. By moving to a more efficient edge architecture that intelligently minimizes the amount of data that moves through the system, we can focus the system’s energy resources on analyzing voice and not on searching for a wake word in irrelevant noise. Analyze, THEN digitize It’s time to move away from the digitize-first approach that has dominated voice wake-up device architecture since the invention of voice-first applications.Inspired by the way the human brain efficiently filters incoming information, differentiating, for example, a dog bark from a baby’s cry, an ultra-low-power analog machine learning technology is changing this paradigm. For the first time, device designers can use low-power analog machine learning to detect which data are important for further processing and analysis prior to data digitization.Leveraging an analyze-first architecture, a new analog neuromorphic semiconductor platform allows the higher-power-processing components in the system to stay asleep until voice has actually been detected, and only then does it wake them to listen for a possible wake word.Delivering a post-microphone audio chain that draws as little as 25µA of current when always-listening and collecting preroll data, this analyze-first architecture allows designers to extend battery lifetime significantly. That’s the difference between smart earbuds that run for weeks instead of hours or a battery-powered smart speaker that runs for months instead of weeks.More importantly, it’s the difference between the current always-listening devices that indiscriminately record and send all sound data to the cloud, and one that has the localized intelligence to select and send only the relevant data, reducing the user’s vulnerability to the loss of private data.Balance convenience with privacyThe trade-off between making our lives easier and keeping our personal information private is a choice that we are asked to make throughout our day in a hundred different ways. Bringing more audio processing capability to the mobile device without draining the battery is the first step toward delivering more secure voice-first solutions. But to succeed in this effort, we must shift to a bio-inspired architecture that determines which data are important and requires further processing at the earliest point in the signal chain. Once we move to the analyze-first approach, only a small fraction of the tens of zettabytes of data collected by the forthcoming generation of always-on IoT devices will require further processing in the device and in the cloud.A better balance between cloud and edge processing is a better balance between convenience and privacy, and that’s a win for everyone.About the AuthorTom Doyle is CEO and founder of Aspinity. He brings over 30 years of experience in operational excellence and executive leadership in analog and mixed-signal semiconductor technology to Aspinity. Prior to Aspinity, Tom was group director of Cadence Design Systems’ analog and mixed-signal IC business unit, where he managed the deployment of the company’s technology to the world’s foremost semiconductor companies. Previously, Tom was founder and president of the analog/mixed-signal software firm, Paragon IC solutions, where he was responsible for all operational facets of the company including sales and marketing, global partners/distributors, and engineering teams in the US and Asia. Tom holds a B.S. in Electrical Engineering from West Virginia University and an MBA from California State University, Long Beach. For more information, please visit https://www.aspinity.com/Technology.Aspinity is a member of MEMS Sensors Industry Group (MSIG), a SEMI technology community, that enables the MEMS and sensor industry to address common challenges, innovate and accelerate business results.

Part 2 of 2-part series on MSEC 2019 highlights. Read Part 1. Neural Networks on ChipTo be sure, low power is king when bringing machine learning to the sensor edge. Battery-powered, always-on sensing devices require it since frequent recharging is the death knell of any electronic product. That’s why semiconductor companies are offering new ways to conserve power.“MEMS sensor suppliers have made significant strides in the power, size and performance of their devices,” said Aspinity CEO Tom Doyle. “Yet these gains deliver only incremental power improvements to the system.”Doyle advocates a new architectural model that uses an analog neuromorphic processor to analyze all sensor data at the start of the signal chain instead of sending it downstream so power-hungry chips such as DSPs can digitize it before analysis.“The technology industry wants to take advantage of the many benefits of always-on sensing applications,” said Doyle. “Before we can reach mass proliferation, however, we need to resolve the power issues that are deal-breakers for some applications. We believe the answer to this challenge is architectural. All the data gathered by always-on sensing systems is analog in nature, yet as soon as it’s captured, it’s digitized immediately for analysis. Determining which data is important up front eliminates the digitization and processing of irrelevant data so that voice-first devices such as smart speakers and wearables/hearables can run for long periods of time without requiring battery recharge.”Syntiant CTO Jeremy Holleman agreed that on-device intelligence is the future.“Did you just fall? Is your heartrate a bit off? Deep learning provides a toolset that yields vastly superior decisions,” said Holleman. “The problem is that deep learning is computationally intensive. The answer is a neural network that performs on-device edge inferencing.”Holleman added that Syntiant’s neural decision processor was recently certified as Amazon Voice Service (AVS)-compliant for wake-word detection, making it easier to design voice control in battery-powered devices such as earbuds and wearables.MSEC Technology Showcase WinnerWith the groundswell of interest in intelligence at the edge, it was no surprise that Cartesiam won top honors among all competitors in the MSEC Technology Showcase for its NanoEdge AI, software that brings AI to the edge of the signal chain, making it easier for designers to create intelligent objects that can learn and understand.“Unlike other AI algorithmic technologies for sensing devices, NanoEdge enables both learning and inference at the edge, providing accurate and adaptive intelligence,” said Cartesiam Managing Director and Co-founder Marc Dupaquier, who accepted the award. “It’s also the only tool of its kind that does not require data scientists on board for implementation, which saves a tremendous amount of money. Our clients can build a machine learning library and embed it into their own code within weeks to realize the same caliber of unsupervised neural network that was once the exclusive domain of AI cloud vendors.”MSIG 2019 Hall of FameAt this year’s conference, MSIG Director Carmelo Sansone recognized two longtime contributors to the commercialization of MEMS and sensors: Peter G. Hartwell, Ph.D., chief technology officer at InvenSense, a TDK group company; and Thomas Kenny, professor and senior associate dean of engineering at Stanford University.Hartwell leads technology strategy and the InvenSense advanced technology research group. He has more than 25 years’ experience commercializing silicon MEMS products, including advanced sensors and actuators, and developing MEMS testing techniques.Kenny’s academic accomplishments include authoring or co-authoring more than 250 scientific papers and holding 50 issued patents. He has also advised more than 50 graduated Ph.D. students from Stanford.MSEC 2020Mark your calendar for next year’s MSEC, October 12-14, at Coronado Island Marriott Resort Spa in Coronado, Calif. Get updates from MSIG on MSEC and other upcoming events including MSTC 2020.Stay in Touch with MSIGMEMS Sensors Industry Group (MSIG), a SEMI Strategic Association Partner, is the industry association representing the global MEMS and sensors supply chain. To learn how MSIG enables professionals in the MEMS and sensors industry to innovate, address common challenges and accelerate business results, visit us today.Connect with MSIG on Twitter and LinkedIn. Subscribe to SEMI Blog: Technology and Trends.Maria Vetrano is a public relations consultant at SEMI.

Software for sensors has evolved from simply reading out and evaluating sensor data to making intelligent decisions based on that data, a transformation enabled by new software synthesis and artificial intelligence (AI) technologies. Together, they make consumer devices smarter, dramatically improving the user experience through greater interactivity and higher levels of automated personalization.SEMI’s Nishita Rao spoke with Stefan Finkbeiner, CEO and General Manager at Bosch Sensortec, who will explore the topic in his October 23 keynote, How Software Makes MEMS Sensors into Smart Systems, at MEMS Sensors Executive Congress (MSEC), October 22-24, 2019, at the Coronado Island Marriott Resort Spa in Coronado, Calif.Join us at MSEC to meet Bosch Sensortec and other industry influencers driving MEMS and sensors innovations. Registration is open.SEMI: What is the relationship between MEMS sensors suppliers and specialized software synthesis providers?Finkbeiner: Collaboration is a key driver for innovation in sensor software. There are already several fruitful collaborations between MEMS sensors suppliers and specialized software providers, which are mostly startups. Collaborations with providers of simulation and evaluation tools as well as with well-known universities in the field of AI are starting to show positive results.Domain expertise is also critical for developing smart sensor software, making it essential to future sensing solutions.SEMI: How does software synthesis relate to sensor fusion?Finkbeiner: Put simply, software synthesis refers to ways of automatically generating code based on domain knowledge and given constraints for specific product versions. Sensor fusion combines sensor data from different kinds of sources in order to improve the results.Software synthesis techniques enable a level of automation that creates new opportunities for more complex sensor fusion, which was formerly out of reach when using traditional approaches that involved, for example, big data and a large number of potential data sources.The traditional sensor fusion toolset can now be further extended by machine learning techniques that help to determine which sources are more reliable than others and how to combine data streams. This topic and others are still active areas of research. A wearable device with motion detection is a case in point. With unsupervised learning, the device could identify short versus long cyclically repeating motions and treat them differently from other types of motion. SEMI: How is the new software synthesis-AI approach different from previous approaches? To what degree will the new approach open up new applications?Finkbeiner: Traditionally, technology companies have used cloud computing for data storage and machine learning on aggregated user data. In that model, MEMS sensors generate large amounts of data that power-hungry hardware (such as digital signal processors) must process. In addition, machine learning generally requires lots of power-hungry cloud nodes with GPUs. This model, however, is not the best option for many users. Just think for a moment about all the scenarios in battery-powered devices where frequent battery charging frustrates users.Leveraging both software synthesis and AI techniques in MEMS sensors is therefore a very promising approach because it supports improved recognition and learning inside the sensor. This means that user-specific data isn’t transferred to the cloud. Instead, it remains private inside the sensor. This improves existing applications that learn all the time and opens up new opportunities for applications such as smart clothing, predicting a product’s lifespan, detecting whether a window or door is open or closed – all without server connectivity.SEMI: How will such software adapt to the individual user?Finkbeiner: Devices will offer much more personalized information to users. For example, optimizing a step counter to match the height, age or Body Mass Index (BMI) of a user – or to adapt to a user’s environment (is the person running on a beach, hiking up a mountain or strolling in a park?) – will provide more accurate information on calories burned. Not every step is created equal, and both pre-loaded personal data as well as real-world environmental data will prove that some steps consume a lot more energy than others.SEMI: What would you like MSEC attendees to take away from your presentation?Finkbeiner: I want to introduce the journey of software development by illustrating specific use case examples. I would also like to offer my outlook on the role of software and AI in MEMS sensors to help increase their adoption in current and new applications. Ultimately, I think it’s important to raise awareness in our industry on why we should embrace the use of software and AI.Connect with Stefan Finkbeiner at MSEC or via LinkedIn. Get more information on Bosch Sensortec products and solutions online.Stefan Finkbeiner, Ph.D., CEO and General Manager, Bosch Sensortec, was appointed CEO of Bosch Sensortec in 2012. He joined the Robert Bosch GmbH in 1995 and has been working in different positions related to the research, development, manufacturing, and marketing of sensors for more than 20 years. His senior positions at Bosch have included director of marketing for sensors, director of corporate research in microsystems technology, and vice president of engineering for sensors.Finkbeiner received his Diploma in Physics from the University of Karlsruhe in 1992 before studying at the Max-Planck-Institute in Stuttgart, where he earned his Ph.D. in Physics in 1995. In 2015, Finkbeiner received the prestigious lifetime achievement award from the MEMS Sensors Industry Group (MSIG), a SEMI technology community.Bosch Sensortec is a member of MEMS Sensors Industry Group, the industry association representing the global MEMS and sensors supply chain. To learn more about how MSIG enables professionals in the MEMS and sensors industry to innovate, address common challenges and accelerate business results, visit us today.Nishita Rao is marketing manager for technology communities at SEMI.

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.