downloadGroupGroupnoun_press release_995423_000000 copyGroupnoun_Feed_96767_000000Group 19noun_pictures_1817522_000000Member company iconResource item iconStore item iconGroup 19Group 19noun_Photo_2085192_000000 Copynoun_presentation_2096081_000000Group 19Group Copy 7noun_webinar_692730_000000Path
Skip to main content
Default Banner Image

machine learning

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.
Read More
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.
Read More
While Artificial Intelligence (AI) emerged in the 1950s, only in recent years have AI applications proliferated with the explosion of data and continuing improvements in Moore’s law that have driven rising processing speeds. Voice assistants, image analysis software, search engines, and speech and facial recognition systems were among the first applications to use AI. Today, adoption has spread to sectors such as agriculture, cybersecurity, healthcare, software development, e-government and the intelligent enterprise to generate jobs and help spur economic growth. The Edge AI Opportunity and the Microelectronics IndustryAI can be embedded in hardware devices such as advanced robots, autonomous cars, drones or Internet of Things (IoT) applications. Today, according to the EU’s digital strategy, data centres and other centralized computing facilities account for the vast majority – 80% – of AI data processing and analysis, with smart connected objects such as automobiles, home appliances and manufacturing robots that bring the compute function closer to the user representing 20%. The latter, known as Edge AI applications, are powered by edge-based machine learning chipsets, not the AI chipsets designed to run cloud-based machine learning algorithms.The EU’s white paper on AI published in February 2020 anticipates that the way data are stored and processed for AI applications will change significantly over the coming five years as edge computing applications proliferate. Most AI applications need to connect with devices that collect data and manage data flows. When the applications connect with cloud infrastructures to train large volumes of data for a machine learning model, the interface devices often require hardware support. Edge AI can minimize data transport by processing data directly from local devices to accelerate data analysis and decision-making and make data transport or accelerator hardware unnecessary, critical in reducing power consumption and enhancing data security for applications such as autonomous driving. Over the past 40 years, the ICT sector has been continuously increasing greenhouse gas (GHG) emissions despite efforts to shift to renewable energy. Cloud-based AI applications require an ICT infrastructure for high-performance computing and high-speed connectivity. According to MIT Technology Review, data centres’ AI workloads could account for a tenth of the world’s electricity usage by 2025. a mass update of cloud-based AI applications may significantly increase energy consumption, unlike with Edge AI. This is why the strategy for developing Edge AI is well-aligned with the EU’s Green Deal objectives. Europe aspires to play a leadership role in Edge AI to strengthen the sector’s competitiveness and protect the European digital sovereignty. Europe’s strong industrial competencies in embedded systems and microcontrollers will help the region promote development of European domestic AI solutions for emerging high-value IoT applications in industrial processes such as Industry 4.0, Connected and Automated driving (CSA), smart cities, climate action, healthcare, and national defence and security. With this strong strategic position in technology, Europe is well-positioned to invest to become the leader in the Edge AI global market.Preparing the Workforce for the Microelectronics IndustryTo design and manufacture leading Edge AI chipsets, European education providers and industry will need to work closely together to train the current and future workforces. Within the framework of the METIS project, a four-year project co-funded by the European Commission through the Erasmus+ programme, SEMI and imec deployed experts in the field to survey and interview focus groups. The survey identified the following key focus areas for workforce development: 1. True Capability of AI and Data Science With AI’s heavy dependence on data, the workforce of the future must be trained in areas of data science including data integrity to ensure quality, unbiased sourcing, collection and accurate analysis necessary to interpret huge volumes of data. Europe also needs to train the next generation of AI chip designers in data security and privacy – key challenges to the widespread deployment of Edge AI chips. 2. Climate Change, Sustainable Development Goals (SDGs) and Social Inclusion TrainingSince the industry must be able to develop Edge AI solutions to enable the digital transformation while limiting GHG emissions, microelectronics engineers need to be schooled in climate change and understand how their work contributes to meeting the United Nation’s Sustainable Development Goals (SDGs). Workplace diversity and social inclusion are also important target areas for education since Edge AI applications should serve various groups of people with different needs.3. EthicsChip industry workers must also be educated in ethical issues of AI related to the technology’s potential societal impact in the near future[1]. With AI applications capable of monitoring Internet searches based on users’ personal preferences and biases to deliver tailored advertising, news and other information, developers must recognize how the technology can influence thinking and behaviour of individuals and groups. This awareness can help developers strike a balance between supporting commercial interests and societal good so the microelectronics industry can ensure ethical implementation of AI. 4. Cross-disciplinary Skills Required for AIAI development requires a comprehensive, cross-disciplinary skill-set to be able to integrate the work of specialists from diverse educational, cultural and professional backgrounds critical to developing non-biased AI solutions. For example, in addition to technical expertise, microelectronics AI developers must be able to communicate clearly and work in close-knit teams with non-technical experts from business, law, medicine and the social sciences.What’s Next?The microelectronics industry has a tremendous opportunity to develop new chip-based solutions for AI architectures, and apply AI techniques to improve operational efficiencies of design and manufacturing. To seize this opportunity, the industry must work closely with education providers to groom the next generation of skilled workers. This tight collaboration is critical to designing and delivering specialised courses to college and university students as well as engineers now working in the chip sector. The stakes are high. By preparing workers to develop Edge AI chipsets, the microelectronics industry can help the world confront some of the greatest challenges it faces today.For more information, see SEMI Responds to European Commission White Paper on Artificial Intelligence.METIS is a Sector Skills Alliance project co-funded by the European Commission’s Erasmus+ Program and coordinated by SEMI. The four year project, launched in November 2019, will develop a Microelectronics Skills Strategy. Based on the strategy, the METIS project will design 43 training modules for 1,100 hours learning in four key areas of the microelectronics sector.We thank Patrick Blouet (STMicroelectronics) and Jeroen Geusens (imec) for their valuable contributions to this article.[1] Ethics of Artificial Intelligence and Robotics, Stanford Encyclopedia of PhilosophyDr. Yanying Li is senior manager of Collaborative Projects at SEMI Europe.Dr. Pushkar P. Apte is the strategic technology advisor for the Smart Data AI Initiative at SEMI
Read More
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.
Read More
Humanity has survived almost unimaginable challenges over the past 5,000 years of documented human history. From war, famine and natural disasters to the first global pandemic in the last 100 years, more often than not, people have relied on one another to survive and thrive again. As the industry association representing the global microelectronics industry, SEMI has similarly made collaboration and community integral to the fabric of its organization. From helping members to succeed through the COVID-19 pandemic to facilitating member-driven industry standards around environmental health and safety, materials, and manufacturing capabilities, this approach shows members that standing together is better than standing alone.On the eve of the 50th annual SEMICON West (July 20-23, 2020) — the first virtual edition in SEMI’s history — I spoke with SEMI’s vice president of technology communities, Michael Ciesinski, about the role of SEMI in tackling big challenges through an active member community intent on solving problems through collaboration.SEMI: How long have you worked with SEMI and in what capacity?Ciesinski: In January 2016, I started my second tour at SEMI when FlexTech, the industry consortium I’d been leading, became SEMI’s first strategic partner. Nearly two years into that role, SEMI President CEO Ajit Manocha asked me to form Technology Communities to engage members with common interests. After FlexTech, we brought on the Fab Owners Alliance, then MEMS Sensors Industry Group (MSIG), and later the Electronic System Design Alliance (ESD Alliance).SEMI now has more than 20 communities in all, including Smart MedTech, Smart Data AI, Smart Manufacturing, Electronic Materials, and Integrated Packaging, Assembly and Test.SEMI: What is your role with Technology Communities — and how do members stand to benefit?Ciesinski: The leadership of Technology Communities ensures that SEMI’s benefits and services align to our members’ interests so we can provide member benefits that matter most. This spans forming communities where people hold common interests (e.g., advanced packaging) to facilitating standards that will promote intelligence in manufacturing (e.g., data standards for AI and machine learning) as well as providing R D funding.I’m especially proud that over the past three years, SEMI has brought more than $40 million in R D funding to our members, with most grants in the $500,000-$1 million range. We’ve been especially successful in securing funding in flexible hybrid electronics (FHE) through U.S. Army Research Laboratories (ARL), a model we first developed through FlexTech.Two recent recipients of FHE funding, GE Research and ITN Energy Systems, show how the grants are spawning partnership opportunities among commercial enterprises, R D organizations and universities. In developing lightweight, non-invasive wearables, including a human-performance sweat-monitoring patch that remotely analyzes sweat to detect hydration levels and other vital signs, GE Research is using key components such as sensors and lightweight batteries in its designs.ITN Energy Systems designed a flexible all-solid-state lithium battery that’s printed on light, flexible substrates to power small and incredibly thin applications.Universities are also benefiting by plugging into the SEMI ecosystem. In fact, 40-50 percent of funded projects are seeding commercialization by universities. This is another validation that SEMI’s collaborative, community approach to microelectronics is working.SEMI: Position, Timing and Navigation (PNT) is another hot area where SEMI has secured ARL funding. What makes this funding different and why is it important?Ciesinski: The PNT grant makes ARL funding available to the MEMS Sensors Industry Group (MSIG) members through SEMI for the first time. If you’ve ever lost GPS signal while coming out of a tunnel, you know how frustrating that is. For us, that’s an inconvenience, but for a healthcare worker in a remote location who’s waiting for a delivery of medication by drone, it could be life-critical. While that’s just one example of why we need PNT to operate when GPS isn’t available, I can imagine dozens of other important dual-use cases, including autonomous driving.SEMI: How else do Technology Communities benefit under SEMI?Ciesinski: Technology Communities need access to diverse resources to spur continuous innovation. Electronic Materials Group participants, for example, need to stay informed on regulations coming out of Asia, the U.S. and Europe that may affect their businesses. Where else other than SEMI can like-minded stakeholders congregate with people up and down the supply chain to determine whether industry-wide action is needed on regulation?SEMI: What is the importance of SEMI’s global footprint?Ciesinski: I’ve worked with many associations and managed major industry consortia. The clear advantage of SEMI is our global footprint. And that’s vital because microelectronics is a global industry involving a multitude of stakeholders that play essential roles in the supply chain.Let’s say you want to discuss EU regulations on hazardous chemicals. Rather than decipher these complexities alone, you can pick up the phone to speak with someone on SEMI’s European team to learn what’s critical.What if you’d like more information on the 20-plus new fabs that are going up in China? You can explore that question with our SEMI China or SEMI Industry Research and Statistics teams.SEMI: How has SEMI evolved over the years?Ciesinski: SEMI has a long history of providing what the industry cares about. We started in trade shows and pivoted to industry standards. We began with small silicon wafers and wafer carriers, and now within the span of 50 years we’re working on data-format standards that will support the application of AI and machine learning (ML) in the semiconductor industry.While highly varied today, data-format standards will help component manufacturers refine processes to create more efficient solutions. This ARL-funded program, which pairs SEMI members with the grant recipient, Cornell University, may offer dramatic gains in the productivity of semiconductor manufacturing.SEMI: How does SEMI’s approach to COVID-19 reflect core values of collaboration and community?Ciesinski: Together with Ajit Manocha, CMO Terry Tsao and other team members at SEMI, we pulled together a task force to help SEMI members navigate the pandemic.We tapped two existing groups, Environment, Health and Safety (EHS) and Information Technology Leadership (ITL) from the start, documenting their strategic and tactical approaches to help all members through the COVID-19 resource section of our website. The EHS section provides tips on facilities and meetings, employee policies, business travel and communications, while the ITL section lists insights on computing hardware for staff, licensing, networks, security and employee policies.Our EHS leadership team, which includes Entegris, Axcelis, Versum, and Intel, immediately started sharing best practices for sanitizing facilities. As a result of team meetings, SEMI EHS shared best practices on keeping the workforce remote and guidelines for returning people to work safely. From securing PPE and safeguarding employees and visitors by performing thermal scanning to outlining communications around potential employee exposures, EHS has provided meaningful resources for the benefit of all members.SEMI also took immediate steps in the area of advocacy. Our advocacy team in Washington, D.C., together with regional SEMI presidents around the world, have ensured that semiconductor facilities were and still are considered essential businesses in the U.S., Europe and Asia. That’s because microelectronics are foundational to fighting the pandemic.Microfluidics are critical to the Reverse Transcription (RT) Polymerase Chain Reaction (PCR) tests most commonly used for COVID-19. Sensors are embedded in the pulse oximeters that allow patients and healthcare professionals to monitor a vital rubric: oxygen saturation level. If oxygen saturation level drops into the low 90 percentiles or below, it may be time to go to the hospital for treatment.Microcontroller units are essential components in a wide range of hospital equipment, including the ventilators that may make the difference between life and death in the most seriously ill patients.SEMI: How can the ingenuity realized through microelectronics continue to help us tackle other big problems? Ciesinski: We have MEMS and sensors to thank for distributed intelligence, giving us the ability to put sensors anywhere, locally based in the field or in the packaging house.Food production is a prime example. Leveraging miniaturized wirelessly connected sensors, we can trace food through the entire production lifecycle, from the seed in the ground to the food in the warehouse and, ultimately, to the product that lands on the table.From larger enterprise such as IBM Food Trust to small startups, we’re using MEMS and sensors to improve crop yields so we can feed a human population that’s growing each year.There’s a sustainability piece as well. We’re using MEMS and sensors to reduce the amount of fertilizer or other nutrients or chemicals in the soil. That’s good for the environment and for the agricultural workers who labor in the fields.MEMS and sensors can also condense the time it takes to perform a specific task, conserving human resources.SEMI: Where do you think SEMI will go in the next decade?Ciesinski: Ten years from now, I believe we will still have our global footprint in place. I expect it will expand, particularly in Asia.We may also expand into new areas such as Latin America and Central America, which would provide at least two major benefits: People working in microelectronics would, I hope, have access to better quality of life. And diversifying the supply chain would allow nations and regions to have more control over the products they need, from PPE to medications, which may help us to better manage through the next pandemic.I am also hopeful that SEMI will be on the leading edge of helping our members communicate in much different fashion from what we have today. We’re already expanding beyond the paradigm of in-person meetings for standards meetings and conferences. As we move forward, I think we’ll see a hybrid solution to doing business, combining in-person meetings with virtual conferences and digital content that’s available 24/7.Whatever changes we see in SEMI, I’m confident that we will continue to see a global footprint in an industry association that prioritizes connections among members.Engage in the SEMI experience at upcoming SEMICON WestRegister today to hear from keynote speakers such as environmental advocate and former U.S. Vice President Al Gore, futurist and author Steve Brown, and IBM Research senior vice president and director Dr. John E. Kelly III, and Lea Gabrielle, special envoy of the Global Engagement Center for the U.S. State Department, at SEMICON West , July 20-23, 2020. Content will be live streamed and available on-demand. Michael Ciesinski is vice president of Technology Communities for SEMI, the global microelectronics industry association, appointed in August 2018. At SEMI, he directs activity for more than 20 industry groups, oversees the association’s R D funding program, and develops new technology initiatives to serve SEMI’s 2,400 members. Prior to re-joining SEMI, Ciesinski was president/CEO of FlexTech Alliance, an industry consortium focused on new methods of creating electronics. From 1995-2008, Ciesinski served in a similar role at the U.S. Display Consortium (USDC), a private/public partnership chartered with building the infrastructure for electronic display and flexible electronics manufacturing. Both FlexTech and USDC annually sponsor multimillion dollar technology development programs and provide industry technical, financial and market services. Ciesinski is a graduate of the University of Albany, NY, and a former member of the Dean’s Advisory Committee at California Polytechnic State University.Maria Vetrano is a PR consultant at SEMI.
Read More
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.
Read More
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.
Read More
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.
Read More
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.
Read More
Every day it seems like a new portable voice-first device is coming to market. From smart speakers small enough to fit in your pocket to tiny wireless earbuds and voice-activated TV remote controls, we are using voice increasingly to play music, select TV shows, turn on the lights or interact with our smart thermostat. While the popularity of voice-first interfaces has spawned massive diversity in device type, as long as these devices are portable, they have one thing in common: They’re battery-powered, and that could be a problem for consumers who are tired of frequently recharging or replacing batteries. Change the Architecture, Reduce the PowerThe issue lies in the traditional hardware architectures of today’s voice-first devices, which are notoriously inefficient when it comes to power consumption. Such devices rely on a “digitize-first” model of processing voice data in which the heaviest power-consumers, like the analog-to-digital converter (ADC) and the digital signal processor (DSP), do all the heavy lifting up front, right at the start of the audio signal chain. They continuously digitize and analyze 100% of the ambient sound data as they search for a wake word, even if speech is not present and the only sound is noise. Because voice is spoken randomly and sporadically, that continuous digitization of sound wastes up to 90% of battery power.To tackle the battery drain in portable voice-first devices, we need look no further than the human brain. Our brain processes sound very efficiently. Imagine that you are outside your house having a conversation with your neighbor. You are able to focus on what your neighbor is saying because your brain can differentiate between sounds that it should send to the deeper brain for speech processing and sounds that it shouldn’t bother processing further (e.g., dog barks, sirens or car traffic). The brain spends minimal energy up front to decide whether it should spend additional energy on processing down the line. In other words, it saves the most power-intensive processing only for the important sounds.We can mimic the brain’s approach to signal processing by enabling a new “analyze-first” architecture for voice-first devices. This analyze-first approach requires ultra-low-power analog processing technology that can differentiate voice from noise before the sound data is digitized. This keeps the higher-power capabilities in a voice-first system, such as the wake-word engine, in a low-power mode when just noise is present. This approach only wakes up the higher-power chips in the system, e.g., the DSP or ADC, when it detects speech. Like our brain, a voice-first system uses an analyze-first architecture to conserve energy most of the time, saving the heavy lifting, i.e., the wake-word listening, for times when speech is present. The analyze-first architectural approach to always-on listening analyzes the analog microphone prior to digitization, saving considerable power in portable voice-first devices that run on battery. This architectural shift to analyze-first is well worth the investment because it reduces the system’s power consumption in a battery-powered voice-first device by up to 10x. That’s the difference between a portable smart speaker that runs for a month on battery instead of a week or smart earbuds that last for a whole day instead of a few hours on a single charge. Longer battery life in portable voice-first devices generates more good will among consumers, creating another key differentiator for manufacturers engaged in the ultra-competitive race for more users.For more information on the analyze-first architectural approach to voice-first devices, please view our video.Tom 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, visit www.aspinity.com. Aspinity is a member of SEMI-MEMS Sensors Industry Group, which connects the MEMS and sensors supply network, allowing members to address common industry challenges and explore new markets.
Read More