Technology and Trends

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.

Nexperia became a standalone company about four years ago after our divestiture from NXP Semiconductors. Last year we started our journey towards smart manufacturing at our back-end factories in Asia by developing a roadmap to help steer us in the right direction.Our first step to creating a convincing and workable smart manufacturing roadmap was to define the very meaning of smart manufacturing to Nexperia. Since the definition of smart manufacturing varies widely, we started by looking at two different and distinct technology adaptations: Physical automation Data-driven manufacturing, or using analytics at the core to develop and adopt machine learning and artificial intelligence (AI) models It is important to find the right balance of investments between physical automation and data-driven manufacturing to steer clear of deployment inefficiencies since only connected solutions deliver full value. Our approach involved the following high-level steps. Meeting with internal management teams for their inputs and examining factory needs and maturity Meeting with other semiconductor factory operators, subcontractors and partners to review their smart manufacturing approaches and challenges Evaluating our needs and status against the Singapore Smart Industry Readiness Index model Physical AutomationEvaluating the maturity of available solutions and adaptions by the industry and our own shop floor helped simplify the thought process quite well. Logistic automation is not new. Very mature solutions, even for custom layouts and preferences, are readily available. Shop floor automation is far more difficult than logistic automation since variability is simply too high. Traditional shop floor investments were always driven from quality or OEE perspectives and not necessarily very well connected. Our approach is outside-in – deploy logistic automation first and then move to the shop floor.Data-Driven ManufacturingHow smart manufacturing becomes depends on the extent to which a factory is data-driven. Enabling data-driven manufacturing requires foundational investments to improve traceability, connectivity and real-time operations. We believe real-time awareness can drive machine-level and closed-loop process control critical for predictive, cognitive control of the shop floor.Real-Time Awareness and Traceability is at the CoreDeveloping real-time awareness requires wide-ranging manufacturing protocols. The following focus areas have helped us simplify the challenge: Connectivity Core systems for areas including MES, quality and SAP Analytics and AI Digital shop floor featuring one operator interface with real-time control systems Readiness of engineers, technicians and managers Each of these pillars has different level of complexity due to legacy equipment and systems, legacy processes and inexperience of engineers with automation. This makes deployment of data-driven operations a complex challenge. We looked at different project approaches for each of the focus areas: Core Systems – Build additional technology enablers and roll them out with prioritization planning. Analytics – Focus mainly on OEE and yield with automated root cause analysis and predictive approaches. Real-Time Control – Merge the initiative with factory-level programs to improve productivity and quality. With a strong smart manufacturing roadmap, the next challenge is to secure long-term buy-in on the plan and required investments from executive management. Visiting and otherwise connecting with peer sites that have already deployed smart manufacturing infrastructure is vital in this effort. Thanks to SEMI members, we were allowed to visit their factories with our management team for go-and-see tours since seeing is believing in the smart manufacturing journey. Our executives also met with subcontractors and vendors to better understand the value of this transformational undertaking.A long-term outlook is necessary to successfully develop a smart manufacturing roadmap, and executive commitment goes a long way to ensuring its success. We are excited about our smart manufacturing journey and believe it is a game changer for our factories.Adarsha MARPALLI is director of Factory Automation at Nexperia B.V.

If you think the world is flooded with a mind-boggling volume of digital content, then you might be just a amazed to learn about the sheer wealth of information and business opportunities that will be uncovered at this year’s SEMICON Japan as the event goes full digital.To start, more than 160 companies will exhibit their semiconductor manufacturing gear and services on the virtual show floor of Japan’s premier event for the semiconductor manufacturing and design supply chain. Add to that over 80 presentations and panels that feature global industry executives, visionaries and experts offering insights into the latest microelectronics developments, trends and technologies, and it’s easy to see how SEMICON Japan 2020 Virtual is designed to help attendees grow their businesses and the industry drive the next wave of innovations that promise to address some of the world’s greatest challenges across healthcare, the environment, transportation and other industries.Best of all, it will all be available at your convenience from your office or home 24 hours a day, making it safe and easy for you and others from all over the world to attend. Following is what’s in store at SEMICON Japan 2020 Virtual to help lead you into the future.Leading Japanese Securities Analysts to Weigh in What’s Ahead for the Chip Equipment Sector in 2021 For the first time, SEMICON Japan will feature Bulls Bears as Japan’s’ five top securities analysts focus on the 2021 outlook for the global semiconductor equipment sector. The December 17th event will include discussions on the COVID-19 pandemic’s impact on the semiconductor industry, the continuing geopolitical tensions that are forcing the industry to reconfigure its supply chains, the fast-growing China market and cutting-edge applications that are powering industry growth. The perspectives from Japan’s investment community are sure to be compelling as the region supplies one-third of the global semiconductor industry’s chip manufacturing equipment.Moderated by Akira Minamikawa of OMDIA, the panel will include these experts:Three Visionaries to Explore the Digital TransformationPowered by semiconductors, the fourth industrial revolution is driving digitalization globally, remaking societies to bring more efficiencies and conveniences to our work and home lives and help more people prosper. But the flip side of those tremendous benefits is the risk that wealth will be concentrated in the hands of people in positions of power, companies and nations. Democratizing economic development remains a serious challenge worldwide.Addressing this pressing issue, the Opening Panel on December 11 will feature prominent visionaries from political, academic and industrial communities including the following:Sony’s Leading-Edge Electric Car and Nissan’s Driver Assistance System to Highlight Automotive InnovationsCars are becoming more like smartphones on wheels, rapidly filling with more and more semiconductor chips every year with electrification and electronic driver-assisted systems to key drivers of this growth. At the SMART Mobility 1 session on December 14, two pioneering companies – Sony and Nissan Motor – will focus on both areas of semiconductor innovation.Sony’s Vision-S concept car, exhibited at CES 2020, astonished many in the electronics ecosystem and the automotive industry. What is Sony’s vision behind the vehicle? Izumi Kawanishi, Senior Vice President, AI Robotics Business at Sony will share the latest on the initiative.Nissan, maker of the pioneering LEAF electric vehicle, is the first Japanese carmaker to equip a car – its new Skyline – with the ProPILOT 2.0 driver assistance system for hands-off highway driving. Nissan Executive Vice President Asako Hoshino will provide an update on the company’s driver assistance system strategy and plans.Quantum Computing Meets Chip Manufacturing for the First Time at SEMICON Japan In contrast with current computer systems that use bits (binary 0 or 1 state) for computing, quantum computers leverage quantum superposition (0 and 1 states exist at once) to quickly solve highly complex problems that might take traditional supercomputers hundreds or even thousands of years to tease out. American physicist Richard Feynman promoted quantum computer as early as 1982, but it wasn’t until nearly two decades later and long after his death that quantum bit circuits emerged for use in superconductive materials.With quantum circuits and devices requiring state-of-art semiconductor processing technology, The Era of Quantum session on December 15 at SEMICON Japan 2020 Virtual will discuss necessary advances in chip manufacturing technology to enable the next generation quantum computing. The session will be the first time SEMICON Japan connects the semiconductor manufacturing and quantum computing communities.The program will feature the following experts:Strategies for Sustainable Semiconductor Industry GrowthSemiconductors are giving rise to a hyper-connected world that is fueling demand for staggering volumes of chips, pressuring the electronics industry to uncover new ways to increase manufacturing efficiency while reducing power consumption in a bid to help combat climate change. The Grand Finale Panel composed of executives from Japan’s semiconductor supply chain and a supervising ministry will gather for the Grand Finale Panel on December 18 to discuss ways the industry can achieve sustainable growth through innovation with a focus on energy savings and an new process technologies such as extreme ultraviolet lithography (EUV), which promises to enable electronics devices that are more power powerful, cheaper and more energy-efficient.Panelists include the following:Register TodayThe SEMICON Japan 2020 Virtual All-In Pass provides online access to all 80 presentations and panels, which will be available on-demand for replay until January 15, 2021. What’s more, all eight keynote programs will feature English subtitles. For complete information of the exposition, programs and registration, visit the SEMICON Japan website.I look forward to seeing you virtually at the event!Jim Hamajima is president of SEMI Japan.

D-SIMLAB Technologies, a Singapore-based provider of simulation-based business analytics and optimisation software solutions, recently joined SEMI. I spoke with Peter Lendermann, the company’s co-founder and Chief Business Development Officer, about the company’s role in the smart manufacturing movement, how customers are benefiting from D-SIMLAB solutions, and what the future holds for smart manufacturing. Ng: What is D-SIMLAB’s mission?Lendermann: Our mission is to develop, market, and deliver high-performance simulation-based decision support solutions that enable corporations to enhance their performance in a sustainable manner leading to significant cost savings. In particular, we focus on semiconductor manufacturing material flow planning and optimisation but also do business in aviation where we help customers optimise their spare parts support operations. What these two domains have in common are three important attributes: They are capital intensive, their underlying operations are complex, and operations are also heavily affected by random, i.e. unpredictable events, which makes both planning and execution of manufacturing operations very challenging. D-SIMLAB is a spin-off from the Singapore Institute of Manufacturing Technology (SIMTech) under the Agency for Science Technology and Research (A*STAR). Our head office is in Silicon Island Singapore. We also have representations in Germany and the U.S. Most of our staff are industrial and computer engineers with up to 20 years of operations experience in their respective industry domain, as well as vast data analytics and software development capability.Ng: What solutions does D-SIMLAB offer to optimise semiconductor manufacturing?Lendermann: In the three-pillar smart manufacturing framework of Connect, Sense and Predict advocated by SEMI, our focus is on Predict though we emphasise the equal importance of the subsequent Act: Our solutions can Predict, for example, WIP waves or usage-based preventive maintenance due dates. But much more value-add can be realised once some decisions with regard to how to Act can be derived from such a prediction. The ability to pro-actively adjust action plans in a timely manner is essential to overcoming challenges arising from changing customer due dates, mix profile changes, untimely production line issues, and production capacity to be shared with R D lots effectively, so that ultimately our customers can enhance capacity, reduce cycle times and improve the due-date performance of their factories.To that end, our D-SIMCON solution suite spans the full spectrum of decision-support tools required to forecast, manage and optimise material flow – from operational scheduling and dispatching, WIP forecasting and dynamic and static capacity planning all the way to specific applications for fab load mix optimisation or for the enhancement of the product/layer dedication and resist allocation in the lithography area. Our solutions are implemented in numerous 6-, 8- and 12-inch wafer fabs operated by both IDMs and foundries worldwide with capacity ranging from 40,000 to 200,000 wafers per month.Ng: What are the key enablers of D-SIMLAB’s success?Lendermann: Our success lies in deploying production-ready solutions for our customers, allowing them to extract immediate value. Our solutions enable the portrayal of many domain-specific characteristics such as queue time constraints or specific equipment behaviour, which is absolutely essential to generating operationally feasible plans or schedules in order to be able to Act in the best possible manner according to what has been Predicted. Moreover, we have modules for automatic generation, calibration and maintenance of the underlying capacity model, including resolution of data inconsistencies as well as verification and validation of the model, to allow near real-time responses to continuously changing operations. And the associated optimisation approaches focus on creating maximum possible value with as few iterations as possible and within minimum time through smart heuristics and parallel computing infrastructure – a paradigm that is as powerful as it is cost-effective.Ng: What are a few of your more notable customer successes?Lendermann: As a result of the first implementation of our novel, multi-objective based Scheduler cum Dispatcher, a tool capacity gain of 8%, a transportation capacity gain of 10%, and an operator workload reduction of 25% were concurrently realised at one of the critical equipment groups in our customer’s fab. At another set of equipment groups in the same fab, a 7% increase of lots within the critical queue time limiting area was achieved.Another use case we successfully realised is fine-tuning of Preventive Maintenance plans: Based on a seven-day lot arrival forecast at each equipment generated with our WIP Forecaster, a recommendation is made when PM would be best possible without causing too much disruption in the WIP flow. The effect of this synchronisation of the PM plan with material flow enabled a dramatic reduction of the average queue lengths at critical equipment groups and the associated cycle times without incurring any capacity loss. Reduction of average queue length as a result of synchronising preventive maintenance with material flow. Ng: What challenges has D-SIMLAB been facing in the COVID-19 world?Lendermann: Obviously, software delivery projects have become more challenging for the time being since our engineers cannot be on-site frequently. But it also turned out that more and more services can be delivered remotely, which has the nice side effect of making the services more cost-effective for customers. Overall, we are confident that our solid customer base will enable us to sail steadily through these challenging times.Ng: Where does D-SIMLAB see the technological development heading?Lendermann: In the future, enriching decision support and manufacturing execution solutions with machine learning and other AI techniques will be critical in reducing dependency on human experience. This path is essential to making manufacturing operations fully Industry 4.0-compliant. D-SIMLAB will certainly be at the forefront of this development. Bee Bee Ng is president of SEMI Southeast Asia.

The air we breathe is precious yet neglected as anthropogenic pollutants continue to pour into the earth’s atmosphere. Still, there’s hope that greenhouse gas emissions – and the human behavior behind them – can be brought under control for the good of the planet with the help of gas sensors that gauge pollutant levels.Of the many air pollutants, some are more detrimental to our health than others. Figure 1 lists the top seven pollutants, their chief sources and health effects. The Air Quality Index is calculated by combining values from particles and four gases (carbon monoxide, ozone, sulfur dioxide, nitrogen dioxide). The good news is that gas sensors are available in the market that can monitor each of those pollutants.Figure 1 – Top seven pollutants and their health effects. Source: EPA Air Sensor Guidebook The challenge is that many gas sensor end users today have little understanding of how to compare the performance characteristics of sensors offered by various vendors. SEMI is working to help end users clear that hurdle. SEMI-MSIG this year created a group within its Device Working Group focused on developing gas sensor standards aimed at growing the market and defining guidelines affecting areas including testing methods, reliability requirements, packaging and communication interfaces. Importantly, the standards will also make it easier for end users to make a clear choice among rival products.The SEMI-MSIG Device Working Group comprises devoted experts from leading gas sensor companies as well as OEMs. We welcome companies involved in deploying gas sensors to join this fast-growing group to improve air quality standards in sectors including residential construction, factory automation, automotive, consumer electronics and healthcare. One potential market is consumer electronics such as smart phones since concerns about air quality is growing among device users.The MEMS Sensors Industry Group (MSIG) Device Working group was formed in early 2019. Its mission is to develop a series of technical specifications, industry standards and best practices for MEMS and Sensor devices and platforms. The goal is to advance the use and expansion of MEMS and sensors worldwide.Table 1 – Top seven pollutants and their health effects. Source: EPA Air Sensor Guidebook In the past, we focused on inertial sensors (See IEEE2700 standard for inertial sensors as an example of an output of this team). In 2020, our focus shifted to gas sensors and we plan to expand our work to include other types of sensors in the near feature. Industry leaders such as Bosch, TDK Invensense, Renesas, Infineon, Analog devices, STMicroelectronics, GE and Intel meet every month to strategize on a series of initiatives.If you’re interested in joining the SEMI-MSIG Device Working Group, please contact Carmelo Sansone, Director of MEMS Sensors Industry Group.The MEMS Sensors Industry Group (MSIG) is a SEMI technology community that enables the MEMS and sensor industry to address common challenges, innovate and accelerate business results.Carmelo Sansone is director of the SEMI-MSIG. He has focused his career on building products and system solutions that have large impact in the marketplace. Sansone launched several sensor processor platforms for low-power applications, including the first microcontrollers with DSP capabilities, the core of today’s portable devices intelligence. Sansone has led the successful integration of the MSIG organization into SEMI by expanding its services and global reach. Carmelo holds a master’s degree in Electronic Engineering with a specialization in Biomedical from the University of Pisa and an MBA from Golden Gate University, San Francisco.