sensors

Over the last three years the number of battery-operated electronic-component solutions for the Internet of Things (IoT) and Industrial IoT (IIoT) applications has been increasing steadily. This trend will continue for years to come, particularly with the growing popularity of mobile devices of all flavors. Addressing power consumption for battery-powered always-on IoT/IIoT devices – which rely on dozens of electronic components, including sensors — is critical to their commercial success.The demand for ultra-low-power sensors has accelerated the race to squeeze every last mW from components. Compared to previous-generations of sensors, semiconductor suppliers have managed to drastically reduce power by as much as 50%-60% over older solutions. Leveraging new state-of-the-art analog design techniques, we have effectively optimized capacitive readings of MEMS structures. How effective are they? We estimate that with the right mix of our company’s power-saving technologies, it is possible our customers could save 3MW/year globally[1].What’s next?While the semiconductor industry continues to investigate novel technologies, approaches and analog IP for greater energy efficiency, we believe that bigger gains in reducing power consumption will come from thinking at the system level. The sensor node is a good place to start.A typical IoT node is composed of a set of sensors, a microcontroller, a radio frequency (RF) link, and a power-supply system, often based on Li-Po batteries.Of these, the microcontroller and RF link consume the most energy and, in the RF link, power consumption is a function of the distance between end point and receiver and of the amount of data transmitted. Thus, at longer distances reducing the amount of data transmitted can save power. We can achieve this by including some pre-elaboration capabilities on-board and by extracting more meaningful information from the raw sensor data.We address this by moving some computation and data analysis inside the sensors, where smart hardware “digital blocks” perform faster and more efficiently than software-based routines running in the microcontroller. We can achieve this by using dedicated hardware resources to reduce overall system power consumption. The beauty of this solution is that it allows the microcontroller to operate in low-power states by only transmitting significant information in batches. The SensorTile development kit can speed up prototyping of ultra-low-power IoT devices by integrating an ultra-low-power MCU and BlueNRG Bluetooth radio with sensors. Some examples of these advanced digital blocks are the Advanced Embedded Pedometer, the Finite State Machine and Decision Tree, and Compressed FIFO in an IMU.The Advanced Embedded Pedometer is a hard-wired step counter that works independently inside the sensor, without CPU intervention: By comparing sensor outputs to pre-defined and -loaded patterns, it autonomously decides whether the user is walking or running to start and stop counting the user’s steps. The sensor then makes this information available to the microprocessor for further elaboration or for simple notification to the user.The Finite State Machine and Decision Tree are new functions dedicated to pattern recognition (machine learning) and decision-making: They can perform complex classifications and state detection, and can send dedicated warning and signaling to the microprocessor. A good real-world example is industrial predictive maintenance, where the sensor can categorize and identify different malfunctioning states in the equipment before waking the microprocessor to react.Our products, on average, save about 1 mA (1e-3) over competitive devices or over our previous-generation parts. So 2.0 x 1e-3 x 1.5e9 = 3MW. Programmable Sensor and Decision Tree Finite State Machine Integrating programmable sensors and decision trees as well as finite state machines in the sensor allows the sensor to do more of the work while the MCU sleeps. Source: STMicroelectronics Another example is compressed FIFO (first-in, first-out) buffer, which can store sensor data in the sensor, not in raw format, by using efficient compression algorithms. In addition to saving memory (and therefore silicon area) inside the sensor chip, it also saves power by reducing the number of bytes transferred to the processor and by shortening the communication data flow, which reduces processor-active time.These examples – the Advanced Embedded Pedometer, the Finite State Machine and Decision Tree, and compressed FIFO buffer – are just some showing that we can develop low-power IoT/IIoT devices through intelligent management of sensors, microcontrollers and other components in any given system. Your starting point is an IoT/IIoT node that lets you selectively allocate some power-hungry tasks — such as computation and data analysis — to sensors instead of the microcontroller. Leveraging data blocks that reside in the sensors alleviates the microcontroller’s typical power drain, allowing the microcontroller to operate with maximum efficiency.[1] ST sells about 1.5 billion pieces/year (1.5e9), which typically run from a 2V supply. Luca Fontanella joined ST Microsystems in 1995 as an analog designer. In 2001 he joined the MEMS team in a marketing role and today he is marketing manager in the MEMS Sensor Division. Luca has contributed to 25+ international patents and has presented at multiple conferences. He earned a degree in Electronic Engineering from Padua University. Simone Ferri joined STMicroelectronics in 1999 as Central R D engineer, moved to the Audio Division as a digital designer and is now director of the Consumer MEMS Business Unit. He holds a degree in Electronic Engineering and an MBA from the Polytechnic of Milan. _______________________________________________________________________________________________Brush up on the latest MEMS and sensors trends and gain a new perspective on emerging applications. Register today!

Sensors are inextricably linked to the future requirements of partially and fully autonomous vehicles. From highly granular dead-reckoning subsystems that rely on industrial-strength gyroscopes for superior navigation to more intelligent and personalized cockpits featuring intuitive human machine interfaces (HMIs) and smart seats, new generations of partially and fully autonomous cars will use sensors to enable dramatically better customer experiences.Dead reckoning, or, where am I, exactly? Dead reckoning is the process of calculating one’s current position by using a previously determined position, and advancing that position based upon known speeds over a time slice. As a highly useful process, dead reckoning is the basis for inertial navigation systems in aerospace navigation and missile guidance, not to mention your smartphone.Today’s best-in-class MEMS gyroscopes can offer 30-50 cm resolution (this is the yaw rate drift) over a distance of 200 meters — a typical tunnel length where a GPS signal is lost. For semi-autonomous (L3) or autonomous (L4, L5), the locational accuracy is well below 10 centimeters; that’s an accuracy usually reserved for high-end industrial or aerospace gyroscopes with a raw bias instability ranging from 1°/h and down to 0.01°/h. These heavy-duty gyros command prices from $100s up to $1000s. Current performance levels of different gyroscopes by application and performance measure in terms of bias drift (IHS Markit). This poses an interesting potential opportunity for both industrial-performance MEMS-based gyroscope sensor-makers, such as Silicon Sensing Systems, Analog Devices, Murata, Epson Toyocom and TDK InvenSense, and for broader-based sensor component-makers such as Bosch, Panasonic, STMicroelectronics, and TDK (InvenSense and Tronics).While MEMS can master performance, size and low weight, cost remains the challenge. The fail-operational mode requirement for autonomous driving will accommodate higher prices, at least in the beginning, probably in the $100+ range at first, even for the relatively low volumes of self-driving cars anticipated by 2030. Nonetheless, automotive volumes are very attractive compared to industrial applications and offer a lucrative future market for dead-reckoning sensors.Your cockpit will get smarter Automakers are banking on the idea that people like to control their own physical environment. Interiors already feature force and pressure sensors that provide more personalized seating experiences and advanced two-stage airbags for improved safety. In some vehicles, automakers are using pairs of MEMS microphones for noise reduction and image or MEMS infrared sensors for detection of driver presence. Eventually, we might see gas sensors that monitor in-cabin CO2 levels, triggering a warning when they detect dangerous levels that could cause drowsiness. These smart sensors would then “tell” the driver to open the window or activate an air-scrubbing system in a more complex solution. While today’s CO2 sensors are still relatively expensive, we may see them designed-in as lower-cost versions come to market.Future cockpits will need to go beyond such concepts in the lead-up to fully automated driving. Seats could contain sensitive acceleration sensors that measure heart and respiration rates as well as body movement and activity. Other devices could monitor body humidity and temperature.We need look no further than Murata, a supplier initially targeting hospital beds with a MEMS accelerometer as a replacement for pulse oximeters. That same Murata accelerometer could be placed potentially in a car seat to detect heart rate. It’s not the only way to do this: another sensing approach for heart-rate measurement comprises millimeter wave radiation, a method that can even look through objects such as books and magazines.Augmenting sensor-based body monitoring, automotive designers will use cameras to fuse information such as gaze direction, rate of blinking and eye closure, head tilt, and seat data with data gathered by sensors to provide valuable information on the driver’s physical condition, awareness and even mood. Faurecia’s Active Wellness concept—unveiled at the 2016 Paris Motor Show—proves that this technology might be coming sooner than we think. Active Wellness collects and analyzes biological data and stores the driver’s behavior and preferences. This prototype provides data to predict driver comfort based on physical condition, time of day, and traveling conditions, as well as car operating modes: L3, L4 or L5. Other features such as event-triggered massage, seat ventilation and even changes in ambient lighting or audio environment are possible. Faurecia’s “cockpit of the future,” announced at CES 2018. (Faurecia) Meanwhile, there are other commercial expressions of more advanced HMI as well as plenty of prototypes. Visteon’s Horizon cockpit can use voice activation and hand gestures to open and adjust HVAC. Capacitive sensors are already widely used for touch applications, and touchless possibilities range from simple infrared diodes for proximity measurement to sophisticated 3D time-of-flight measurements for gesture control.Clearly, automotive designers will have a lot more freedom with HMI in the cabin space, providing a level of differentiation that manufacturers think customers will appreciate—and for which they will pay a premium.Managing sensor proliferationResearchers are investigating ways to solve the issue of high-functionality vehicles containing myriad sensing inputs, i.e., when we have so many sensing inputs, designers must address wiring complexity and unwanted harness weight. Faurecia, for example, is considering ways to convert wood, aluminum, fabric or plastic into smart surfaces that can be functionalized via touch-sensitive capacitive switches integrated into the surface. These smart surfaces could reduce the explosion of sensing inputs, thereby diminishing wiring complexity. With availability from 2020, Faurecia’s solutions are approaching the market soon.Beyond functionalized switches, flexible electronics and wireless power sources, and even energy harvesting (to mitigate power sources), could provide some answers. Indeed, recent research has shown that graphene-based Hall-effect devices can be embedded in large-area flexible Kapton films, and eventually integrated into panels. OEMs such as Jaguar Land Rover are interested in such approaches to address the downsides of electronics and sensor proliferation, especially in luxury vehicles. While smart surfaces would represent a big change in sensor packaging and a disruption in current semiconductor processes, they remain a long way from commercial introduction.By 2030 or thereabouts, fully autonomous cars that detect our mood, vital signs and activity level could well be available. Cabins could signal us to open the window if CO2 levels become dangerous. HVAC systems could increase seat ventilation or turn up the air conditioning (or the heat) based on our body temperature. Feeling too hot or too cold in the cabin could become a thing of the past, at least for the driver, whose comfort level is the most important! We could feasibly feel more comfortable in the car than in our office, our home or at the movies. Perhaps our car will become our office, our entertainment center and our home away from home as we take long road trips with the family, without a single passenger uttering, “Are we there yet?” Bio: Richard Dixon, Ph.D., is a senior principal analyst for MEMS research at IHS Markit and author of more than 50 MEMS-related consulting and market research studies. He is a renowned expert on automotive MEMS and magnetic sensors used in safety, powertrain and body applications. Along with supporting the overall activities of the MEMS and sensors group, his responsibilities include the development of databases that forecast the markets for more than 20 types of silicon-based sensors in more than 100 automotive applications. In addition, he has supported organizations with future scenarios for sensors in cars and has supported many custom projects for companies in the automotive supply chain.In his prior post at Wicht Technologie Consulting (WTC), Dixon was a senior MEMS analyst where he led research on physical sensors and was the co-author of the NEXUS Task Force Report for MEMS and Microsystems 2005-2009. He has also led commercialization and road-mapping activities on European Commission-funded technology projects, including detailed MEMS chip cost analysis studies.Dixon worked previously as a journalist in the compound semiconductor industry and has five years of experience as a technology transfer professional at RTI International, where he provided business and market intelligence for early-stage technologies.Dixon graduated from University of Greenwich with a degree in materials science and earned a doctorate from Surrey University in semiconductor characterization. He speaks English and German.For more information, visit https://technology.ihs.com/Categories/450486/mems-sensors. ___________________________________________________________________________________________________ Want to hear more from IHS Markit on MEMS and sensors devices and their applications? Top thinkers from IHS Markit will be speaking at upcoming SEMI events. Register today!Disruption in the authentication sensor market Manuel Tagliavini, Principal Analyst, MEMS Sensors, IHS Markit Autonomous and Electric Cars: What's in for Conventional MEMS SensorsJeremie Bouchaud, Director and Senior Principal, MEMS Sensors, IHS Markit

2017 was a good year for the MEMS and sensors business, and that upward trend should continue. We forecast extended strong growth for the sensors and actuators market, reaching more than $100 billion in 2023 for a total of 185 billion units. Optical sensors, especially CMOS image sensors, will have the lion’s share with almost 40 percent of market value. MEMS will also play an important role in that growth: During 2018–2023, the MEMS market will experience 17.5 percent growth in value and 26.7 percent growth in units, with the consumer market accounting for more than 50 percent(1) share overall. Evolution of SensorsSensors were first developed and used for physical sensing: shock, pressure, then acceleration and rotation. Greater investment in R D spurred MEMS’ expansion from physical sensing to light management (e.g., micromirrors) and then to uncooled infrared sensing (e.g., microbolometers). From sensing light to sensing sound, MEMS microphones formed the next wave of MEMS development. MEMS and sensors are entering a new and exciting phase of evolution as they transcend human perception, progressing toward ultrasonic, infrared and hyperspectral sensing.Sensors can help us to compensate when our physical or emotional sensing is limited in some way. Higher-performance MEMS microphones are already helping the hearing-impaired. Researchers at Arizona State University are among those developing cochlear implants — featuring piezoelectric MEMS sensors — which may one day restore hearing to those with significant hearing loss. The visually impaired may take heart in knowing that researchers at Stanford University are collaborating on silicon retinal implants. Pixium Vision began clinical trials in humans in 2017 with its silicon retinal implants.It’s not science fiction to think that we will use future generations of sensors for emotion/empathy sensing. Augmenting our reality, such sensing could have many uses, perhaps even aiding the ability of people on the autism spectrum to more easily interpret the emotions of others.Through my years in the MEMS industry, I have identified three distinct eras in MEMS’ evolution: The “detection era” in the very first years, when we used simple sensors to detect a shock. The “measuring era” when sensors could not only sense and detect but also measure (e.g., a rotation). The “global-perception awareness era” when we increasingly use sensors to map the environment. We conduct 3D imaging with Lidar for autonomous vehicles. We monitor air quality using environmental sensors. We recognize gestures using accelerometers and/or ultrasonics. We implement biometry with fingerprint and facial recognition sensors. This is possible thanks to sensor fusion of multiple parameters, together with artificial intelligence. Numerous technological breakthroughs are responsible for this steady stream of advancements: new sensor design, new processes and materials, new integration approaches, new packaging, sensor fusion, and new detection principles.Global Awareness SensingThe era of global awareness sensing is upon us. We can either view global awareness as an extension of human sensing capabilities (e.g., adding infrared imaging to visible) or as beyond-human sensing capabilities (e.g., machines with superior environmental perception, such as Lidar in a robotic vehicle). Think about Professor X in Marvel’s universe, and you can imagine how human perception could evolve in the future! Some companies envisioned global awareness from the start. Movea (now part of TDK InvenSense), for example, began their development with inertial MEMS. Others implemented global awareness by combining optical sensors such as Lidar and night-vision sensors for robotic cars. A third contingent grouped environmental sensors (gas, particle, pressure, temperature) to check air quality. The newest entrant in this group, the particle sensor, could play an especially important role in air-quality sensing, particularly in wearable devices.Driven by increasing societal concern over mounting evidence of global air-quality deterioration, air pollution has become a major topic in our society. Studies show that there is no safe level of particulates. Instead, for every increase in concentration of PM10 or PM2.5 inhalable particles in the air, the lung cancer rate is rising proportionately. Combining a particle sensor with a mapping application in a wearable could allow us to identify the locations of the most polluted urban zones.The Need for Artificial Intelligence To realize global awareness, we also need artificial intelligence (AI), but first, we have challenges to solve. Activity tracking, for example, requires accurate live classification of AI data. Relegating all AI processing to a main processor, however, would consume significant CPU resources, reducing available processing power. Likewise, storing all AI data on the device would push up storage costs. To marry AI with MEMS, we must do the following: Decouple feature processing from the execution of the classification engine to a more powerful external processor. Reduce storage and processing demands by deploying only the features required for accurate activity recognition. Install low-power MEMS sensors that can incorporate data from multiple sensors (sensor fusion) and enable pre-processing for always-on execution. Retrain the model with system-supported data that can accurately identify the user’s activities. There are two ways to add AI and software in mobile and automotive applications. The first is a centralized approach, where sensor data is processed in the auxiliary power unit (APU) that contains the software. The second is a decentralized approach, where the sensor chip is localized in the same package, close to the software and the AI (in the DSP for a CMOS image sensor, for example). Whatever the approach, MEMS and sensors manufacturers need to understand AI, although they are unlikely to gain much value at the sensor-chip level.Heading to an Augmented WorldWe have achieved massive progress in sensor development over the years and are now reaching the point when sensors can mimic or augment most of our perception: vision, hearing, touch, smell and even emotion/empathy as well as some aesthetic senses. We should realize that humans are not the only ones to benefit from these developments. Enhanced perception will also allow robots to help us in our daily lives (through smart transportation, better medical care, contextually aware environments and more). We need to couple smart sensors’ development with AI to further enhance our experiences with the people, places and things in our lives.About the authorWith almost 20 years’ experience in MEMS, sensors and photonics applications, markets, and technology analyses, Dr. Eric Mounier provides in-depth industry insight into current and future trends. As a Principal Analyst, Technology Markets, MEMS Photonics, in the Photonics, Sensing Display Division, he contributes daily to the development of MEMS and photonics activities at Yole Développement (Yole). He is involved with a large collection of market and technology reports, as well as multiple custom consulting projects: business strategy, identification of investment or acquisition targets, due diligence (buy/sell side), market and technology analyses, cost modeling, and technology scouting, etc.Previously, Mounier held R D and marketing positions at CEA Leti (France). He has spoken in numerous international conferences and has authored or co-authored more than 100 papers. Mounier has a Semiconductor Engineering Degree and a PhD in Optoelectronics from the National Polytechnic Institute of Grenoble (France).Mounier is a featured speaker at SEMI-MSIG European MEMS Sensors Summit, September 20, 2018 in Grenoble, France. (1) Source: Status of the MEMS Industry report, Yole Développement, 2018

Ahead of his presentation on the future of wearables at the European MEMS Sensors Summit 2018, 19-21 September in Grenoble, France, SEMI spoke with Dr. Peter Weigand, vice president, Business Strategy and Portfolio Management, Bosch Sensortec GmbH. Dr. Weigand gave a glimpse into insights he’ll share at the event.1. Wearables such as smartwatches, fitness trackers or hearables are becoming ubiquitous – but what are the must-haves for wearables for daily use by wearers?We see that users nowadays want to track their activities such as steps walked, calories burnt and floor levels “climbed” on a daily and holistic basis. “Quantifying yourself” is becoming an overall trend in our society with health, fitness and well-being continuously gaining in importance. This is only possible if information about activities is delivered comprehensively in an accurate manner. Therefore, at Bosch Sensortec we provide MEMS sensors that measure the user’s activity very precisely. For example, the smart sensor hubs BHI260 and BHA260 provide sophisticated in-sensor algorithms (e.g. activity recognition) with very low latency and guaranteed performance due to the real-time nature of the embedded software. From the system manufacturer’s perspective, “quantifying yourself” on a 24/7 basis means that the device has to be “always-on.”However, these always-on functions usually consume a lot of battery power, which poses challenges to the manufacturers and system designers, as the battery capacity is usually small due to the size of the wearable. This shows two other must-haves for the users nowadays. First, the compact size of the device. While smartphones have become larger, users of wearables benefit from the devices’ small size and their low weight, offering the possibility to wear them directly on the body. Therefore, we design the footprint and height of our MEMS sensors as small as possible to ensure the compact size and the ease of integration into new, stylish types of wearables. For example, the BMP388, measuring only 2 x 2 x 0.75 mm³, qualifies as the world’s smallest barometric pressure sensor. The second requirement in this regard is long battery life. Users do not want to charge their wearable device every other day, as this would also impede the always-on activity tracking aspect. At Bosch Sensortec, we hence provide MEMS sensors that run at ultra-low power to ensure always-on endurance and a long battery life. The BMA400 is an ultra-low power acceleration sensor that draws ten times less current than existing accelerometers.2. Are there any other user requirements for wearables?Yes, we see for example that just tracking the number of steps or the calories burnt is not enough anymore. Users require multi-functional devices that also provide information that can be used to monitor sleeping behaviour, navigate in cities, or prepare your smart home for your arrival. We are equipping our sensors with more features and developing new types of sensors that add new functionalities to wearable devices. For example, we have developed a smart watch Projection Module that can project information on the back of the user’s hand for an additional, enlarged display. While smart watches are rising in popularity, demand for basic wristbands is waning. Users are paying more attention to device design. Like clothing, the look and feel of the device should support the user’s individual style.At the same time, with more fashion brands are entering the wearables market we are providing sensors that are easy to integrate into new types of wearables such as hybrid watches. Our products feature a small form factor to ensure flexible, simple design-in. For example, the new BMA400 acceleration sensor easy to design into various applications. Finally, to conform to the user, the wearable must adapt to the user’s individual habits and motions such as learning different gestures, requiring the devices to be not only smart but increasingly intelligent with artificial intelligence (AI). We are providing sensors, such as the BHI260, with embedded, local intelligence with advanced algorithms that enable devices to learn. We are developing intelligent software solutions that use deep learning, enabling device to adapt to the user’s individual behaviour.3. What current techniques are design engineers using to reduce power consumption of wearables?Several techniques are being developed to reduce power consumption. The goal largely is to reduce the power draw of components that are always-on, such as the screen in a smartwatch. In activity trackers, the motion sensor is always on to sense, track, classify and store motion data. Reducing the power needed to operate these features will cut total system power consumption as well. A good example is our BMA400 accelerometer that has a current consumption of less than 1 µA in full operation.At the same time, it independently processes sensor data. For example, the device converts the three-axis motion sensor data stream into step counting events. This allows the main (host) microcontroller to remain in the stand-by mode required for activity tracking and to be activated by the accelerometer to deliver full power only, say, every 100 steps. The sensor, rather than the microcontroller, manages the overall duty cycling of the microcontroller to reduce system power and increase overall efficiency.4. What alternatives are engineers exploring to reduce power consumption? What is the role of intelligence directly within sensors for local processing capabilities in wearables?We have seen how the BMA400 can reduce power by integrating the motion classification functions. We can take this concept further by integrating a microcontroller that’s specifically tailored for low-power sensor data processing, such as the “fuser core” that Bosch Sensortec uses within its smart sensor hubs such as BHI260 or BHA260. The built-in sensor data fusion and machine learning hardware accelerators make it uniquely suited to reduce overall system power. The concept of edge computing has been around for many years, but only in this and the previous sensor generation with built-in local intelligence are we reducing the full power profile of the wearable device. Our sensor architecture design allows us to process the power locally in the MEMS sensor without waking up the main application processor.5. What technologies are you developing to lengthen battery life without compromising performance? We are continuously improving the MEMS and ASIC designs of our sensor portfolio to drive ever higher power efficiency. The BMA400 draws 10 times less current than existing accelerometers while delivering solid high performance (e.g. low-noise data). 6. Wearable device feature and performance requirements are continuously rising. Will batteries need to be larger to support these requirements? Since the beginning of the portable consumer electronics, improving batter life and reducing chip power consumption have been parallel efforts, a trend we expect to continue. However, we expect a greater focus on the overall system power reduction with sensors managing the power, turning on and off microcontrollers, radios (including GPS) and displays in wearable devices.7. What do you expect from European MEMS Sensors Summit 2018 and why do you recommend attending in Grenoble?The European MEMS Sensors Summit is a very important platform for us. It is an opportunity to meet partners, customers, industry leaders, to exchange ideas and to get new insights and thus to ultimately refine our solutions for our global customer base. Our ultimate goal is to improve people’s individual lifestyle and well-being.Serena Birschetto is a marketing communications manager in SEMI Europe.

Driven by the adoption of evermore electronic components in end products, the semiconductor industry is facing a new era in which device scaling and cost reduction will no longer continue on the path followed for the past few decades. Advanced nodes no longer bring the desired cost benefit, and R D investments in new lithography solutions and devices below 10nm nodes are rising substantially. In order to satisfy market demands, the industry is looking for technology solutions to bridge the gap and improve cost/performance while at the same time adding more functionality through integration.More than-Moore devices (including MEMS and sensors, CMOS Image Sensors, power electronic, along with RF devices) represent this new functional diversification of technologies, combining performance, integration and cost not limited to CMOS scaling, and their importance will become more and more preponderant.In 2017, wafer demand for More than Moore devices1 reached almost 45 million 8-inch eq. wafers. This figure is expected to reach more than 66 million 8-inch eq wafers by 2023, showing an almost 10 percent growth during this period.This increase is supported by the famous megatrends detailed in the new analysis, Wafer Starts for More Than Moore Applications2, performed by Yole Développement (Yole). This analysis is relevant to the following markets: 5G with wireless infrastructure and mobile segments, mobile including additional functionalities, voice processing, smart automotive and electrification, AR/VR3, and AI4.For the first time, the market research and strategy consulting company presents a dedicated technology and market analysis focused on the overall wafer demand for More than Moore devices. The aim of this report is to give an overview of wafer shipments for More than Moore devices, from wafer size to semiconductor material substrate type including silicon, glass, SOI5, SiC6, SiGe7, GaN8, InP9, GaAs10, sapphire and ceramic, and thus identify business opportunities in the More than Moore industry.For over 20 years, Yole has been analyzing the industry evolution, discussing with leading companies to understand market challenges, and identifying technical breakthroughs. The Wafer Starts for More Than Moore Applications report is the result of this 20-year research. Yole’s analysts combine technical and market expertise to describe the More than Moore world. Market size (volume and value), substrate sizes and formats, value chain, technology processes and market drivers, business opportunities and competitive landscape are all part of Yole’s analysis.The various research teams at Yole, encompassing power electronics, imaging and sensing, RF and semiconductor manufacturing, collaborate to present an in-depth understanding of the current market evolution, taking into account innovations and emerging businesses. This methodology allows Yole to cover the overall megatrends and illustrate the links between wafer substrate, device, module, sub-system, system and high-end product.Under this dynamic ecosystem, the deployment of renewable energy sources and industrial motor drives as well as the electrification of the automotive industry are good examples of the impact of megatrends on the semiconductor industry development. They are directly impacting the power devices’ wafer market, resulting in an expected 13 percent CAGR between 2017 and 2023. Already in 2017, this market represented more than 60 percent of the overall wafer market for More than Moore devices, and is currently still dominating the More than Moore industry.5G is one megatrend driving wafer demand. 5G is leading the More than Moore evolution, bringing any service to any user, anywhere. Antennas and filtering functionalities are two of the key innovations of this evolution.Without doubt, the stringent requirements of 5G are driving increasing demand for RF components like RF filters, power amplifiers (PAs), and low-noise amplifiers (LNAs) to ensure access to tomorrow’s radio network.This year, Corning and Menlo Micro announced a major agreement to develop a DMS[11] product platform. Both partners propose an innovative approach based on TGV12 packaging technology. According to both partners, this technical choice allows them to cover operation of frequencies beyond 50GHz. Amongst the numerous megatrends, mobility is not far behind 5G. Demand for advanced mobile applications integrating more and more functionality is growing. In order to compete companies are developing smart combinations of devices such as fingerprint sensors, ambient light sensors, 3D sensing, microphones, and inertial MEMS devices. As an example, impressive developments focused on SOI-based NIR sensors have been released by SOITEC for front-side imager applications including advanced 3D image sensors. This technical evolution will clearly contribute, in the near future, to strong growth of the wafer market for MEMS and sensors. Additionally, the automotive industry, with the development of smart cars, has reached a new level of complexity requiring the development and integration of new sensors. In this context, many companies are aiming to extend their capabilities in ADAS13 and autonomous driving. Recently the leading company On Semiconductor acquired SensL Technologies, the leader in SPAD and LiDAR sensing products for automotive. This acquisition is one sign among many highlighting the evolution of this historic industry, searching for new expertise and welcoming new players, more aware of consumer habits and needs.Yole’s analysts expect smart automobiles to drive consistent growth of CIS14 and sensor wafer production over the next five years. It is fueled by the increasing integration of high-value sensing modules like RADAR, imaging, LiDAR and more. Although automotive will be mainly supported by these growth areas, historic MEMS and sensors such as MEMS pressure sensors and inertial MEMS will continue growing at a reasonable rate, supporting the standard automotive world.Yole Group of Companies including Yole, System Plus Consulting, KnowMade, PISEO and Blumorpho follows and analyzes the industry continuously. The Group has developed in-depth expertise and knowledge focused on the semiconductor manufacturing process and markets. Companies of the Group work together to understand the technical issues, identify business opportunities and propose valuable analyses.Yole invites you to an overview of the Wafer Starts for More Than Moore Applications report during the exclusive online event, titled “Wafer Starts for More than Moore Applications – Webcast”. This hourlong webcast takes place on June 28 at 5:00 PM CEST. The market research company will present key results of this report including megatrends, wafer market evolution and technical trends. Moderated by David Jourdan, Sales Coordination Customer Service at Yole, it welcomes the two leading companies, SPTS (an Orbotech Company) and Corning Precision Glass Solutions: "Trends in Wafer Processing Technologies for RF MEMS" – Speaker David Butler, Executive Vice President and General Manager at SPTS Technologies "Benefits of Through Glass Vias for RF applications" – Speaker: Ravij Parmar, New Product Development Manager for Corning Precision Glass Solutions These results will be also presented by the Semiconductor Software team at SEMICON West (Booth #1320), SEMICON Taiwan and SEMICON Europa (Booth #A-4667). Make sure to meet Yole’s analysts and get a valuable overview of the More than Moore industry. Agenda and more information are available on i-micronew.com. Stay tuned!About the authors:Amandine Pizzagalli is a Technology Market Analyst, Equipment Materials - Semiconductor Manufacturing - at Yole Développement (Yole). Amandine is part of the development of the Semiconductor Software division of Yole with the production of reports and custom consulting projects. She is in charge of comprehensive analyses focused on semiconductor equipment, materials and manufacturing processes. Emilie Jolivet is Director of the Semiconductor Software Division at Yole Développement, part of Yole Group of Companies, where her specific interests cover package assembly, semiconductor manufacturing, memory and software computing fields. 1 Including: MEMS sensors, CIS, and power, photonics and RF devices2 Yole Développement, March 20183 AR/VR : Augmented Reality/Virtual Reality4 AI : Artificial Intelligence.5 SOI : Silicon On Insulator6 SiC : Silicon Carbid7 SiGe: Silicon Germanium8 GaN: Gallium Nitride9 InP: Indium Phosphide10 GaAs : Gallium Arsenide111 DMS : Digital-Micro-Switch12 TGV : Through Glass Via13 ADAS : Autonomous Driving Assistance Service14 CIS : CMOS Image Sensor

Self-driving cars have been all the rage in both the trade and popular press in recent years. I prefer the term “autonomous vehicles,” which more broadly captures the possibilities, encompassing not only small passenger vehicles but mass transit and industrial vehicles as well. Depending on who’s talking, we will all be riding in fully autonomous vehicles in five to 25 years.The five-year estimates come from startups eager to raise venture capital while the 25-year estimates stem from Tier 1 automotive suppliers who tend to be more conservative in outlook. Regardless of the timeframe, a multitude of investors – national governments, venture capitalists and companies – are dedicating significant capital and effort to make autonomous vehicles a reality.I must admit that I did not fully grasp the enthusiasm for self-driving cars until last year. First, I’ve always enjoyed driving, unless I’m in stop-and-go traffic, so I couldn’t imagine relinquishing the task. Second, I’ve deliberately arranged my life to spend minimal time in my car. However, traffic has become much heavier in my metropolitan area (Boston), and I know that many people in cities around the world face longer commutes and waste more time in gridlock.What is the solution to this problem that is only getting worse? I had an epiphany while walking through Shinigawa Station in Tokyo, one of the busiest train stations in the world. Dense streams of people crisscrossed the station on their individual paths, managing to avoid collisions without the aid of traffic controls. Evidently, humans have an innate collision-avoidance ability that makes traffic controls for pedestrian crowds unnecessary. If autonomous vehicles could achieve the same excellence in collision-avoidance, we could potentially reduce or eliminate traffic controls for vehicular traffic, providing a huge gain in transportation efficiency and relief from gridlock.Sensors as core building blocksNew and improved sensors, many based on micro-electromechanical systems (MEMS) technology, are key to achieving this vision. While MEMS inertial sensors (such as accelerometers and gyros) are already integral to the core safety systems in conventional vehicles, they are also essential to improved self-navigation in autonomous vehicles.The challenge for MEMS suppliers is to deliver inertial sensors that meet the requirements for self-navigation systems, which are different and more demanding than for safety systems.Pinpointing a vehicle’s position requires “dead reckoning” based on inertial sensor signals as a supplement to GPS input. Undesirable drift in the inertial sensor signals due to mechanical quadrature, temperature sensitivity and noise can quickly add up to a large error in position that may result in a collision. To meet the more rigorous requirements for autonomous vehicles, suppliers must design MEMS inertial sensors that are substantially more precise and resistant to drift. This requires design software that is both extremely accurate and fast, as well as increasingly precise and reliable manufacturing capabilities.Other MEMS-based devices, such as micromirrors and micro ultrasound transducers (MUTs), are also promising options for implementing vision and range-finding systems in autonomous vehicles. These sensing systems are needed for building electronic versions of the human collision-avoidance abilities that I witnessed in Shinigawa Station – and it is these systems that autonomous vehicles must emulate.When will self-driving cars become a reality? Aside from the provocative question that got you to read this far, I don’t have a definitive answer. It will undoubtedly occur in phases, ranging from the driver-augmentation systems available in today’s cars to the full autonomy and ubiquity that will allow reduction of traffic controls in 20 years or more. It is clear that the ultimate goals for autonomous vehicles are highly worthwhile, and that achieving those goals will require better-performing and more diverse MEMS sensors. Stephen (Steve) Breit, Ph.D. is Senior Director, MEMS Business, at Coventor, a Lam Research Company. Steve has been responsible for overseeing development and delivery of Coventor’s industry-leading software tools for MEMS design automation since joining Coventor in 2000. Steve holds numerous patents on software systems and methods for MEMS design automation and virtual fabrication. He holds a Ph.D. in Ocean Engineering from MIT and a B.S. in Naval Architecture and Marine Engineering from Webb Institute.For more information, visit: https://www.coventor.com

With technology moving at breakneck speed, MEMS and sensors professionals whose job is to stay on top of industry developments must be able to find useful information—fast. Podcasts are one rich source of insight. In All Ears, I share roundups of recent podcast interviews with entrepreneurs and CEOs and episodes covering emerging technologies, breakthroughs and even the unexpected – like a MEMS pinball machine. For the seasoned MEMS and sensors professional or the curious onlooker who loves to learn, here are 5 podcast episode recommendations. 1. Embedded.fm – Episode 214: Tiny Sensor ProblemsChristopher White (@stoneymonster) and Elecia White (@logicalelegance) host Embedded.fm, a weekly podcast about the 5Ws of engineering. They’re both embedded software engineers by trade and their guests include everyone from entrepreneurs and makers to educators and engineers.Tiny Sensor Problems is a good introduction for people who have little to no knowledge of MEMS sensors. Kristen Dorsey, Assistant Professor of Engineering at Smith College, provides a brief overview of MEMS and touches on the manufacturing processes, including temperature sensitivity and sensors hype over the years. You’ll learn facts about interesting MEMS applications that were created, like the pinball machine I mentioned. Dorsey also elaborates on her work in flexible strain and pressure sensors for possible applications in AR and robotics in the future. 2. NPT – Episode 4: MEMS Directional SensorsLet’s dig deeper and learn about some of the applications for MEMS and Sensors. In this case, Erdos Miller, The Drilling Technology Podcast focuses on an extreme niche: oil and gas drilling technology. Ken Miller and David Erdos make up two of the engineering, developers and architect team at Erdos Miller that specializes in creating custom solutions for oil and gas downhole devices. Throughout the episode, they explore surveying sensors starting from the 1920s. History buffs would appreciate the stroll down memory lane and the ingenuity behind the first survey sensor, which involved a glass bottle filled with acid. Texas Instruments’ DLP technology gets a mention towards the end of the episode when micromirrors became a topic of discussion. 3. The Early Stage Podcast – Episode 15: Vesper – Tiny Microphones That Listen ForeverMEMS and sensors are a huge part of IoT—no doubt about it. The Early Stage Podcast captures insights from entrepreneurs into their company’s journey including their innovative approaches to developing cutting-edge technologies and overcome business and technology challenges they encounter. This episode focuses on Matt Crowley, CEO at Vesper, and how piezoelectric microphones will affect the voice interfaces as AI grows more sophisticated. Enthusiastic about the subject, John Valentine, host of the Early Stage Podcast, poses thoughtful questions and Crowley is eloquent and clearly passionate about his trade. They touch upon the race to produce the best voice interfaces for the AI ecosystem and tool kits for companies interested in voice enablement—but lacking a dedicated audio team—and looking for a simple solution. 4. IoT Podcast – Episode 155: New toys, Pi Day and insect-tracking LIDARHost Stacey Higginbotham, a technology journalist covering cloud computing, data centers and IoT, joins IT expert and veteran podcaster Kevin Tofel, in a weekly conversation about IoT developments. They’re entertaining and informative with a knack for making complex concepts easily digestible. In this episode, they discussed their thoughts on how the Broadcom/Qualcomm merger played out. While not explicitly focused on MEMS and sensors, the episode and the podcast in general touches upon overarching challenges the MEMS and sensors industry faces with security, standards, product development and applications usage. The highlight of the show included the guest of this week, Tobias Menne, global head of digital farming at Bayer AG who discusses Agriculture Technology (AgTech). 5. Amelia’s Weekly Fish Fry – Silicon Stagnation: How Emerging Technologies and Non-Traditional Materials Are Changing the Future of MEMSHosted by visionary Amelia Dalton, this episode of Fish Fry addresses the prospect of paper and plastic displacing silicon in MEMS manufacturing. Dalton interviews A.M. Fitzgerald Associates founder Alissa Fitzgerald about her research on the threat of waning research to silicon sensor technology. And more importantly, they discuss its implications for the MEMS and sensor industry 10 to 20 years down the line.Can’t get enough of MEMS? Register to listen in on MEMS and Sensors Industry Group’s free webinar, “Process Control and Root Cause Analysis for More-than Moore and Advanced IC Technologies” on April 25 at 8:00 AM PDT.

Artificial intelligence (AI) is making headlines everywhere, offering a range of capabilities, including location and motion awareness — determining whether a user is sitting, walking, running or sleeping. Behind the scenes, AI is capturing volumes of data. Makers of smartphones and fitness and sports trackers, along with application developers, are all clamoring for this data because it helps them analyze real-world user behavior in depth. Manufacturers gain a competitive edge by tapping this intelligence: Using it to improve user engagement, they increase the perceived value of their devices, potentially reducing customer churn. How can consumer-product manufacturers tap the built-in capabilities of MEMS inertial sensors — which are already ubiquitous in end-user devices — to make the most of AI? Machine learningProduct manufacturers can easily build an activity classification engine using commonly available smart sensors and open-source software. Activity trackers, for example, use raw data first collected via the MEMS inertial sensors that are already installed in smartphones, wearables and other consumer products.With the building blocks in place, consumer-product manufacturers can apply machine learning techniques to classify and analyze this data. There are several possible approaches, ranging from logistic regression to deep learning neural networks.One well-documented method used for classifying sequences in AI is Support Vector Machines (SVM). Physical activities, whether walking or playing sports, consist of specific sequential repetitive movements that MEMS sensors gather as data. MEMS sensors make good use of this collected data, which can be easily processed into well-structured models that are classifiable with SVMs.Consumer-product manufacturers have gravitated toward the SVM model since it is easy to use, scale and predict. Using an SVM to set up multiple simultaneous experiments for optimizing classification over diverse, complex real-life datasets is far simpler than with other approaches. An SVM also introduces a wide range of size and performance optimization opportunities for the underlying classifier.Cost impacts of processing, storage and transmissionIn practice, recognizing user activity hinges on accurate live classification of AI data. Therefore, the key to optimizing product cost is to balance transmission, storage and processing costs without compromising classification accuracy.This is not as simple as it sounds. Storing and processing AI data in the cloud would leave users with a substantial data bill. A WiFi, Bluetooth or 4G module would drive up device costs and require uninterrupted internet access, which is not always possible.Relegating all AI processing to the main processor would consume significant CPU resources, reducing available processing power. Likewise, storing all AI data on the device would push up storage costs.Resolving the issuesTo resolve these technology conflicts, we need to do four things to marry the capabilities of AI with MEMS sensors.First, decouple feature processing from the execution of the classification engine to a more powerful external processor. This minimizes the size of the feature processor size while eliminating the need for continuous live data transmission.Next, reduce storage and processing demands by deploying only the features required for accurate activity recognition. In one example created by UC Irvine Machine Learning Repository (UCI), when an AI model was trained using a dataset of activities with 561 features, it identified user activity with an accuracy of 91.84 percent. However, using just the 19 most determinative features, the model still achieved an impressive accuracy of 85.38 percent. Notably, pre-processing alone could not identify these determinative features. Only sensor fusion enabled the data reliability required for accurate classification. Third, install low-power MEMS sensors that can incorporate data from multiple sensors (sensor fusion) and enable pre-processing for always-on execution. A low-power or application-specific MEMS sensor hub can slash the number of CPU cycles that the classification engine needs. The onboard software can then directly generate fused sensor outputs at various sensor data rates to support efficient feature processing.Finally, retrain the model with system-supported data that can accurately identify the user’s activities.Additionally, cutting the data capture rate can reduce the computational and transmission resource requirements to a bare minimum. Typically, a 50 Hz sample rate is adequate for everyday human activities. This may soar, however, to 200 Hz for fast-moving sports. Reducing dynamic data rate selection and processing in this way lowers manufacturing costs while making the product lighter and/or more powerful for the consumer.High efficiency in processing AI data is key to fulfilling its potential, driving down costs and delivering the most value to consumers. MEMS sensors, in combination with sensor fusion and software partitioning, are critical to driving this efficiency. Operating at very low power, MEMS sensors simplify application development while accurately analyzing motion sensor data.Combining AI and MEMS sensors into a symbiotic system promises a new world of undreamt-of opportunities for designers and end users.This blog post is based on an original article that first ran in EDN. It appears here with the permission of the publisher.