MEMS & Sensors Executive Congress

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