GPS

System level electromagnetic (EM) simulation is becoming increasingly important in supporting the design of smart cities, next-generation transportation and global communications systems. CEMWorks' develops tools for electromagnetic field simulation that overcomes limitations in today’s solvers.

System level electromagnetic (EM) simulation is becoming an increasingly important technology needed to support the design of next-generation smart cities and transportation as well as global communications systems. CEMWorks is on the cutting edge of EM in-house simulation research.

PNI Sensor, a member of the SEMI-MSIG Positioning, Navigation and Timing (PNT) Technical Advisory Council, is developing advanced tracking systems that promise to increase industrial worker safety.The availability of low-cost GPS jamming and spoofing technologies renders GPS-only solutions for location and navigation an increasingly dangerous and ineffective choice for the dismounted soldier in a battlefield environment. This threat to armed forces has spurred development of new self-contained location and navigation technologies for defense applications — an innovation that offers significant advantages for commercial applications.Though not as complex and mission-critical as in defense, self-contained location technology is also essential in commercially available industrial applications. That’s particularly true for workers in industrial sectors such as utilities, mining, and construction, and in environments with lone or remote workers, such as first responders. While jamming and spoofing are not a threat in the industrial sector, determining the precise location of workers in GPS-denied environments is fundamental to ensuring their safety. This makes it a priority to adapt any self-contained, non-infrastructure-based location technology — which was first developed for the modern dismounted soldier — to industrial applications.Bodies in MotionInertial solutions are very difficult to implement properly, even without the challenges uniquely created by human motion dynamics. On a construction site, for example, workers tend to cover a wide range of disciplines: supervisors, electricians, iron workers and equipment operators, among others. While performing their jobs, construction workers change locations, both indoors and outdoors, and perform dynamic motion such as crawling, ducking and climbing. These are all motions that are very difficult to model using traditional adaptive filtering techniques, which are typically applied in vehicular inertial navigation platforms, such as aircraft, ships and tanks. Even if existing inertial navigation systems could be made size, weight, power and cost (SWaP-C)-compatible to be body-worn, their performance accuracy would still need to satisfy the application’s requirements. To properly determine a worker’s precise location to ensure safety on job sites and in remote locations, we must tackle the combined challenges of SWaP-c and human dynamic motion. That’s the most effective approach for creating a complementary positioning technology that augments GPS or other infrastructure-based location systems.To address these challenges, we need to build a high-performance inertial measurement solution using commercially available MEMS inertial sensors. The issues of bias drift error and low sensitivity have traditionally made such sensors practically useless for any meaningful inertial tracking. Fortunately, this is no longer the case. We now have sensors that already conform to the necessary SWaP-C requirements for the application, and have the additional advantage of high dynamic range of measurements without saturation errors, which helps to reduce high-force and rapid movement-induced errors, promoting greater accuracy.Thus, a path forward is emerging. The current generation of high-performance MEMS gyros can now inertially track workers’ locations to step-level resolution very well for up to 30 minutes — without significant location errors due to bias or scale errors. That’s an order of magnitude better than previous generations. With the new MEMS gyros, errors typically remain less than 2% of distance travelled over that time period. Strategically applying algorithm improvements with higher levels of magnetic corrections has the potential to bring that accuracy down even lower, to less than 0.5% of distance traveled for durations of one hour or more. What’s more, the improved gyro and accelerometer bias, gain, and signal-to-noise (SNR) performance allows for better magnetic anomaly rejection. This enables finer and more sustained gyro bias corrections in the fused solution, which creates a system greater than the sum of its parts. We believe that these newer systems will promote greater worker safety at a truly affordable price.PNI Sensor, a member of the SEMI-MSIG PNT Technical Advisory Council (TAC), is developing a tracking system that combines the best elements of the newest-generation MEMS devices with an electronic compass that uses advanced magnetic anomaly detection and rejection algorithms. Based on PNI’s latest attitude and heading reference system (AHRS), the novel PNT system employs a unique Kalman algorithm that intelligently fuses its reference magnetic sensors with gyros and accelerometers. In conjunction with this work, PNI Sensor has developed advanced pedometry functionality for use in its tracking system for very high dead-reckoning tracking performance used in defense industry applications. PNI is initially designing that system to track dismounted soldiers and special forces operating in GPS-denied or contested environments.For more information about PNI Sensor’s advanced location and navigation technology, please visit PNI Sensor. To learn more about the SEMI-MSIG PNT TAC, please contact Carmelo Sansone, director, MEMS Sensors Industry Group.George Hsu is a founder and CTO of PNI Sensor. He has focused his career on the sensor industry, having invented several magnetic sensor breakthroughs, including the magneto-inductive technology, the core of today’s electronic compassing in the automotive, consumer, scientific and military markets. Hsu is a graduate of Stanford University School of Engineering, holds several patents, and is a much-published author of technical articles on sensor theory, design and applications. He is an active member of the MEMS Sensors Industry Group PNT TAC.About the SEMI-MSIG Positioning, Navigation and Timing ProjectMEMS Sensors Industry Group (MSIG) created a member-based PNT TAC to identify and pursue PNT system innovations for GPS-denied environments. To that end, MSIG solicited proposals from its membership for the SEMI-MSIG PNT Project, a U.S. Army Research Laboratory-funded R D project. PNT committee members that have secured funding are pursuing R D platforms that improve accuracy and performance. Platforms may include software, hardware, and advanced packaging requirements of optical and MEMS-based positioning and timing systems.

Smart technologies have gripped the world’s imagination with their promise to revolutionize the way we live and work. With the semiconductor supply chain central to these advances, SEMI Japan in October hosted 200 members for SEMI Japan Members Day as speakers from three of the world’s top device manufacturers – Denso, Sony and Kioxia – offered their perspectives on the strides the semiconductor industry needs to make in three key areas: automotive, smart manufacturing and 3D flash memory manufacturing technology. Automotive Evolution and Electronics – DensoThe automotive industry is re-inventing itself to innovate across connectivity, autonomy, sharing and electric (CASE) and ensure safe, comfortable and environmentally friendly autonomous driving, said Nobuaki Kawahara, executive fellow and director of the Advanced Research and Innovation Center at Denso. Key focus areas of Denso in CASE innovation are Extraordinary Safety and Everyday Confidence. The company’s goal is to minimize damage to vehicles involved in collisions or one-car accidents by making it easier for drivers to detect and steer clear of objects in their path.To improve automobile safety and security, the company is developing advanced driver-assistance systems (ADAS) and autonomous driving technologies as it promotes the confluence of four areas of technology – HMI (Human Machine Interface), environmental recognition, vehicle control assistance, and information and communications. One use case Denso sees as a significant opportunity is deploying sensors such as millimeter-wave radar, cameras and LiDAR to monitor a vehicle’s surroundings, using GPS and precision mapping to pinpoint its location and determine the best route for safety and distance, and then transmitting that information to a motion-control system.Denso is also out to solve the hard challenges associated with autonomous driving in dynamic road conditions. Kawahara pointed out that road conditions vary and that rules for "driving at certain intervals in a certain lane" vary depending on the time of day. Also, on public roads in Abashiri, Hokkaido, where the company is currently conducting field tests, snowfall makes it difficult to recognize road images and gather sensor information. In Asia, it is also common for motorcycles and automobiles to speed along with very little space between them.Image Sensors to Accelerate Development of Smart Manufacturing – SonyTo fulfill the promise of smart manufacturing, the semiconductor supply chain must continue to invest in sensor and imaging technology innovation, said Shigeo Ohba, deputy senior general manager of the Imaging System Business Division at Sony Semiconductor Solutions. For its part, Sony is developing imaging sensors that help network and automate factories to achieve new production and cost efficiencies. For example, the company plans to design devices to increase equipment uptime through predictive maintenance, reduce defect rates and drive other manufacturing efficiencies. The challenge with today’s factory lines that produce a number of different devices is that they are highly complex to manage and therefore prone to human error, undercutting manufacturing efficiency. In the future, AI-powered machines will leverage data analysis to help streamline operations. Adapting an image sensor with AI to machine vision applications can simplify key processes such as measurement and inspection processes while reducing safety and security costs.Of the vast amount of information on all machines connected to the cloud, only essential details will be processed at the edge since edge data processing offers stronger security and reduces data transfer time. Ohba said image sensors will evolve based on edge AI, adding that "AI will be a paradigm shift for image sensors if it’s economically feasible."3D Flash Memory Manufacturing Technology Challenges – KioxiaIncreasing connectivity in factories for smarter, more efficient operations places huge demands on memory since networked devices typically store duplicate data, said Hideshi Miyajima, head of the Advanced Memory Development Center (AMDC) at Kioxia. To meet demand for higher networking speed and capacity, 2d NAND flash memory is moving to 3D and, in particular, three 3D techniques: multivalued memory, cell partitioning and layer stacking.To increase storage capacity, the third-generation 64-layer BiCS FLASH™ stacks layers to form nearly two trillion holes with a diameter of 100nm and a depth of 5μm on a wafer and places a uniform 2-3nm thin film on the inner wall of each 5-μm hole. For its BiCS FLASH™, Kioxia uses a dry etching technique that forms a straight, elongated through-hole and atomic layer deposition (ALD) technology, which creates a uniform laminate atomic layer on the wafer surface to grow materials uniformly and with high precision on large, complex substrates.In order to meet the cost expectations of high-volume 3D flash memory manufacturers, outlays across fabs must be reduced by better monitoring plasma control, enhancing yield through particle control, speeding film formation, and reducing gas, power and water usage, Miyajima said.SMART Transportation and SMART Manufacturing in the Spotlight at SEMICON JapanPlease join us at SEMICON Japan 2019, December 11-13 at Tokyo Big Sight, for the latest developments and trends in SMART Transportation and Smart Manufacturing. There are also a few other great reasons to attend. We look forward to seeing you in Tokyo!Jim Hamajima is president of SEMI Japan.

Tracking and localization technologies typically integrate with Wi-Fi and Bluetooth signals to pinpoint the location of people and objects. But what if a venue can’t install beacons or routers, or afford to deploy Wi-Fi or Bluetooth networks? Thanks to a combination of proprietary algorithms, advanced sensor fusion and the natural geomagnetic field, GipStech, a spin-off of Università della Calabria, built an indoor localization and navigation technology platform for accurate localization in the absence of an adequate GPS signal.Ahead of the SEMI MEMS Imaging Sensors Summit, 25 to 27 September 2019 in Grenoble France, Serena Brischetto of SEMI spoke with Gaetano D'Aquila, co-founder and CEO of GiPStech, about sensor fusion, augmented GPS applications and the future of indoor localization. Join us in Grenoble to learn more about GiPStech and meet other MEMS, imaging and sensors experts. Registration is open online.SEMI: Early this year GiPStech completed a test deployment of the first high-precision, infrastructure-free navigation system at Tokyo Shinjuku metro station in Japan. This is the busiest transportation hub globally! What were the main challenges you faced and how did your technology enable such a highly complex indoor localization?D'Aquila: As you mentioned, Shinjuku station in Tokyo has been registered in Guinness World Records as the busiest transportation hub globally. With 36 platforms, 200 exits and countless corridors and connections, it is easy to get lost there, especially for foreigners and tourists. On the other hand, this scale and complexity makes it unfeasible and expensive to install Bluetooth or similar infrastructure for standard indoor localization.For this reason, we needed to provide a cost-effective indoor localization technology without installing any kind of artificial supporting infrastructure. Thanks to our GiPStech patented multi-sensor-fusion localization stack and the high density of public Wi-FI networks, it’s possible to determine when passengers are inside the station. The public Wi-Fi networks signals were fused as an additional source in GiPStech's sensor-fusion platform to complement the inertial and geomagnetic engine and deliver very accurate results across the entire station. The tests performed in the station also demonstrated that the localization system can even detect the floors where travelers are walking. Now we are ready to roll out the same setup in other stations and environments.SEMI: You are not the first to pursue infrastructure-free indoor localization, but your technology platform seems to be very accurate in bringing precision, stability and consistency to the user experience. What lead to those advancements and incredible results?D'Aquila: Our key differentiating factors are built in the approach we created after years of research and development. One differentiation, of course, is related to our expertise and know-how about how the geomagnetic field can be used as a driving signal for the localization process.During R D we constructed and patented a modular multi-sensor-fusion software stack to solve any kind of localization problem, mainly in indoor environments. We started from a single-signal approach based on the employment of the geomagnetic field as a localization signal. But, mainly due to the very inaccurate devices chosen to measure the geomagnetic field, such as the smartphones that everyone carries in their pockets, we noticed that this single-signal approach is accurate but not reliable because it is strongly affected by a key weakness – the quality of sensor in the device.SEMI: How long did it take for you to solve this issue?D'Aquila: We started to integrate other signals within a few months after the first field tests related to the employment of the geomagnetic field alone. We also began to develop a software platform that could fuse any signal source (natural or artificial) available in the environment to preserve the reliability and accuracy of the localization system when some of these signals are temporarily affected by poor measurement quality. This is our differentiating factor today. We can re-configure our software platform to provide the best reliability and accuracy with the lowest artificial infrastructure in almost any context – from outdoor in a seamless way to indoor and vice versa.SEMI: GiPStech’s inertial engine is one of your cutting-edge technologies that completes your advanced indoor navigation and localization software stack. How do you see the technology evolving?D'Aquila: The inertial engine was one of our first technology modules mainly developed to enhance reliability, smooth the signals and reduce the computational power requirement of our geomagnetic localization approach.After a while, together with a third party that evaluated the performances of our module, we noticed that this module not only can be used as a self-standing localization technique, but it can also deliver high accuracy mainly in PDR (pedestrian dead reckoning) applications.Today our PDR is itself a black box with embedded subsystems. Besides some filtering modules, it includes a state-of-the-art step detector that detect steps even when the person changes the smartphone position and location (not only in the hands but also in backpacks or pockets) and an advanced step validation module that identifies and rejects fake steps.If you’ve ever used a commercial fitness tracker attached to your wrist, you know that in most cases if you move your arm the device will counts some steps that, of course, are not real. Our step validator solves this problem by detecting only real steps – a very important capability that allows our PDR to be employed as a self-standing inertial navigation system. We developed the PDR with strong attention to maintaining low requirements for the computational power and memory footprint. These additional characteristics makes the PDR very interesting even for a direct integration of the software at the silicon level in modern MEMS sensors.In a nutshell, the ability of MEMS sensors to run directly an embedded software module will drive technology enhancements that will allow some of the functionalities now available through an external application processor, such as those in smartphones, to move to a lower level (in the silicon). This, of course, reduces power consumption while even increasing the number of value-added services, including localization services, that could be built directly on top of the MEMS without requiring external software and/or application processor.SEMI: Do you think indoor localization will be more applicable in the next 10 years in areas such as Smart manufacturing, travel, healthcare, entertainment and retail?D'Aquila: Several market reports and our business development experience lead us to assess which sectors are of greatest interest for the application of indoor positioning technologies. They include the following. Industry (manufacturing logistics) Healthcare (tracking of assets, patients and doctors) Big installations (visit experience for museums, fairs) Airports stations (both for travelers and for resource and operation management) Large distribution (user profiling and influencing of the purchasing behavior) Indoor localization is a key enabling technology. Adoption, mainly in these sectors, was limited by the unfavorable tradeoff between cost and benefits. Our indoor localization technology aims to overcome those tradeoffs to make its adoption much more cost-effective while providing the best possible reliability and accuracy.SEMI: What are your expectations regarding the summit in Grenoble, and for the future of the sensors technology ahead? Where are we heading?D'Aquila: Many sectors would benefit from indoor localization technologies. MEMS, imaging and sensors are driving innovation and explosive demand for transportation, medical, mobile, industrial and other IoT applications. But these devices also constitute the basic building blocks for the development of reliable and affordable localization technologies.In outdoor environments we are pretty covered by the GPS. Indoors, where we spend more than of 80 percent of our time, similar types of services are coming to the market now and becoming more reliable over time.This Summit facilitates the direct interaction between different stakeholders to act at different points in the MEMS sensors value chain. Indoor localization was an emerging technology unrelated to the sensors ecosystem until now. Today, indoor localization must leverage MEMS sensors to be effective and reliable. In the future, localization technologies will be embedded directly in silicon to deliver the best performance at a lower cost to increase their adoption for more applications.Gaetano D'Aquila served as research fellow from 2002 to 2004 at the CNR and as an assistant teacher at the University of Calabria. From 2003 to 2014, he worked in the industry first as a security consultant for Telcos and Banking in Value Team S.p.A. and then as project manager at Infomobility S.p.A., where he coordinated research and development and strategic activities in the automotive and auto insurance industries. In 2014 he co-founded GiPStech and is its current CEO. He has published several papers in scientific journals and has filed for seven patents, three of which have been granted in the U.S. and Europe. Gaetano has a MSc in Computer Engineering and a Ph.D. in Science and Engineering of the Environment, Buildings and Energy from the University of Calabria, Italy.Serena Brischetto is a marketing and communications manager at SEMI Europe.

Marcellino Gemelli, director of global business development at Bosch Sensortec, will present at the upcoming MEMS Sensors Executive Congress on October 29-30, 2018 in Napa, Calif. SEMI’s Maria Vetrano caught up with Gemelli to give MSEC attendees a preview of Gemelli’s feature presentation.Sensor fusion — the integration of different types of sensors through software algorithms to increase overall system performance and/or reduce power consumption— has come a long way since its inception. In those early days, sensor fusion generally involved MEMS inertial sensors only. The advent of new sensor varieties, including environmental sensors, is making new use cases a reality. Gemelli will explore the ways in which the next generation of sensor fusion is improving autonomous mobility devices. SEMI: Why are environmental sensors important to autonomous mobility devices?Gemelli: When most of us think of autonomous systems, we think that they are driven by motion sensors and proximity sensors (e.g., radar, Lidar). When vertical location comes into play, however, in applications such as drones or asset tracking, pressure sensors become an integral part of flight control, navigation and positioning in GPS-challenged areas.While not commonly considered an electronically enabled sense, the ability to “smell” the environment opens new opportunities. The quality of a user’s experience with personal cleaning robots and robo-taxis are good examples of where we might want to enable scent detection.SEMI: I’ve never thought much about using sensors to detect smell. How would a robo-taxi or a cleaning robot benefit from scent detection?Gemelli: Fully autonomous cars will inevitably give rise to robo-taxis. In fact, last month Volvo announced its fully electric robo-taxi, and in March 2018 Waymo announced that Jaguar Land Rover’s SUV would join Fiat Chrysler’s Chrysler Pacifica minivans in its planned fleet of robo-taxis, so we may see robo-taxis in the U.S. within the next five years.With robo-taxis fast-approaching, we need technologies that provide the same level of oversight that a taxi driver once fulfilled. Gas sensors would function like an electronic nose (e-nose) in a robo-taxi to inform the taxi’s owner of prohibited passenger behavior, such as eating, drinking or smoking in the vehicle, which could potentially damage the vehicle’s interior. Camera sensors could record the act as proof of the offense.Cleaning robots would be more sophisticated than they are today. In addition to leveraging image and range-finding sensors to more accurately map the rooms in your house, they could also detect scents from spilled red wine, pet urine or other foreign materials. When the cleaning robot, such as a vacuum, detects the foreign substance, it would navigate around the substance instead of going through it and spreading it all over the carpet.In addition to robo-taxis and cleaning robots, I will also discuss asset tracking and drones.SEMI: What role does sensor fusion play in autonomous mobility devices?Gemelli: Combining sensor fusion with artificial intelligence (AI) will generate new use cases and therefore new markets for sensor suppliers.There is another major benefit as well. With so many connected devices in our lives — including those with cameras, location awareness and always-listening capabilities — we are seeing growing concern about user privacy. Sensor fusion and AI can help to alleviate this concern: By supporting more local processing, they allow for greater control of data, safeguarding personal privacy.SEMI: Who is responsible for the AI part of the sensor-fusion equation?Gemelli: AI is a new frontier for MEMS and sensors suppliers. It benefits us and our customers to embrace AI algorithms through in-house development and/or partnerships.SEMI: What would you like MEMS Sensors Executive Congress attendees to take away from your presentation?Gemelli: I plan to issue a call to action to increase research in hybrid sensor-fusion software architectures, including AI, as suppliers’ collaboration will benefit the industry at large.Marcellino Gemelli is currently based in Palo Alto (CA) responsible for business development of Bosch Sensortec's MEMS product portfolio. He received the ‘Laurea’ degree in Electronic Engineering at the University of Pavia, Italy while in the Italian Army and an MBA from MIP, the Milano (Italy) Polytechnic business school. He previously held various engineering and product management positions at STMicroelectronics from 1995 to 2011 in the fields of MEMS, electronic design automation and data storage. He was contract professor for the Microelectronics course at the Milano (Italy) Polytechnic from 2000 to 2002.Marcellino Gemelli will present Environmental Sensors Systems Enabling Autonomous Mobility on Tuesday, October 30 at MEMS Sensors Executive Congress in Napa Valley, Calif.Register today to learn more about the connection between sensor fusion, AI and next-generation autonomous mobility devices.Maria Vetrano is a public relations consultant at SEMI.

Nicolas Sauvage, senior director of Ecosystem at TDK InvenSense, will present at the fast-approaching MEMS Sensors Executive Congress on October 29-30, 2018 in Napa, Calif. SEMI’s Nishita Rao spoke with Sauvage to offer MSEC attendees advance insights on Sauvage’s feature presentation.SEMI: What is “autonomy value” and why is it important?Sauvage: How do you increase the perceived value of an electronic device? If it’s an autonomous car, its value is closely tied to the autonomy level — i.e., the independence — that it offers people. Higher autonomy value for a self-driving car, for example, means that even a blind person could use it. It’s been almost two years since Waymo demonstrated this, and here’s the video that shows it.Countless other sensor-based electronic products have their own “autonomy value.” Imagine the need to get medicine to people during a humanitarian health crisis. Drones could be your best option because they can deliver to inaccessible or remote locations. Unlike older drones, which require active piloting by a person, a drone with higher autonomy value could deliver medicine to Doctors Without Borders without ongoing human intervention.This drone could navigate objects, such as trees and birds, and would have excellent location-awareness. It could fly through any landscape in bright sunlight or during the night. To increase the drone’s autonomy value, you would need better sensors, including those sensors that can enable sensing in sunny conditions or in pitch-black night, as well as better machine learning.SEMI: In this example, what types of sensors would the drone manufacturer need?Sauvage: The manufacturer would need a “surrounding-sensing” solution that includes ultrasonic and pressure sensors as well as image sensors. Start with high-quality image sensors combined with ultrasonic range-finding sensors — high-accuracy devices that function in all lighting conditions and can detect objects of any color. Add motion sensors and a pressure sensor, which would capture the height of the drone to make known the drone’s location in space. The drone would need this combination of sensors, plus smart sensor fusion, because GPS alone cannot avoid obstacles: its signal can be sporadic in certain parts of the world or in certain terrain, making it unreliable.A key attribute of all these sensors would be low power consumption since the drone would run on battery.SEMI: To what extent might autonomy value cause manufacturers to consider multi-vendor solutions?Sauvage: I would like to see it inspire the MEMS and sensors ecosystem to work together, to arrive at multi-vendor solutions that will benefit humanity through greater autonomy value. Whether we’re looking at autonomous cars, drones, robotics or other applications, there are cases where we need to prioritize safety and security over industry competition. SEMI: Where are we today in terms of achieving true autonomy value – and where are we going?Sauvage: The sky is the limit, literally. Machine learning and surrounding-sensing solutions applied to cars, drones and robots will increase autonomy value to the point where we can justifiably call it artificial intelligence.SEMI: What would you like MEMS Sensors Executive Congress attendees to take away from your presentation?Sauvage: I hope that attendees will recognize the value of ecosystem solutions in increasing autonomy value. Together we can expand the variety of sensor types that address novel use-cases and jobs-to-be-done. Instead of waiting for customers to ask for ecosystem-level solutions, we need to articulate a complete MEMS and sensors supply-chain ecosystem if we want the Internet of Things (IoT) and Industrial IoT (IIoT) to grow more quickly. As senior director of Ecosystem, Nicholas Sauvage is responsible for all strategic relationships, including Google and Qualcomm, and other HW/SW/System companies. He is also responsible for strategic and market-driven goal-setting of our SensorStudio developer program, and driving select partnerships with SoC sensor hub platforms. Prior to joining InvenSense, Nicolas was part of NXP Software management team, responsible for worldwide sales, as well as for P L and product management of their OEM Business Line. Nicolas is an alumnus of Institut supérieur d’électronique et du numérique, London Business School and INSEAD. Register today to connect with Nicolas Sauvage at the event. You can also connect with him on LinkedIn.Nishita Rao is a marketing manager at SEMI.