New-Tech Europe Magazine | Sep 2019 | Digital Edition

September 2019

16 IMUs: Let Your Host Sleep with On-Board Machine Learning 22 EUV lithography: weighing the options for future logic and memory applications 26 Bipolar Power Solutions for Precision Test and Measurement Systems 30 Determining the Best Option for NVMe-over- Fabrics

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Contents

14 LATEST NEWS 16 IMUs: Let Your Host Sleep with On-Board Machine Learning 22 EUV lithography: weighing the options for future logic and memory applications 26 Bipolar Power Solutions for Precision Test and Measurement Systems 30 Determining the Best Option for NVMe-over-Fabrics

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34 OUT OF THE BOX 36 NEW PRODUCTS 44 INDEX

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New-Tech Magazine Europe l 9

Latest News

Imec.istart supports nine new startups and goes international

Imec, the world’s leading research and innovation hub in the field of nanoelectronics and digital technology, today announces that nine new tech startups have been selected for the imec.istart accelerator program. In addition to a pre- seed funding of 50,000 euros, they will receive one year of tailor-made coaching for their

municipalities (BrighterBins ), to a middleware solution that makes plug and play blockchain integrations possible (Arkane). The international newcomers are LevelUP Sports, Firmalyzer and Asylia Diagnostics. They focus on – respectively – an app for sports coaches to better visualize and analyze

matches; an innovative platform to detect the safety risks of IoT devices; and a digital platform that helps doctors choose the right cancer therapy for a patient. Sven De Cleyn, program manager imec.istart: “The entry of international start-ups shows that imec.istart is also on the map abroad as a credible business acceleration program. Our international network – which we can count on thanks to imec – is certainly an important asset here. It gives our start-ups access to and insight into international markets, legislation, etc. … important expertise that helps them to break through internationally. ” Entrepreneurs who are also interested in participating in the imec.istart accelerator program can submit their application until 1 October. More info on this link https://www.imec-int. com/en/istart/call stealth mode and announcing that it has completed a seed funding round of $4 million, led by Check Point co-founder Marius Nacht and Israel Discount Capital, Israel Discount Bank Investments Arm, with the participation of investors such as the blockchain VC firm EdenBlock, iAngels, IDEAL-HLS, StratX and Israel Innovation Authority. The company was founded in July 2018 by the CEO Lior Lamesh and the CTO Shahar Shamai, who previously protected the State of Israel’s strategic assets against cyber-attacks. GK8’s advisory board includes cryptography expert and Zcash founding scientist Prof. Eran Tromer and Ilan Levanon, formerly the head of

specific market, in order to make the transition to the market easier. The rising trend in the number of promising international start-ups applying is also continuing. The admission into the program often goes hand in hand with a office opening in Flanders for these young companies, which has a positive impact on job creation, productivity and economic growth for our region. The new startups bring very different products to the market, ranging from an ingenious tool to ensure that meetings run smoothly and efficiently (JUCE +), a smart method for developing biomarkers that detects and treats diseases such as cancers, Alzheimer’s and Parkinson’s faster and more accurately (ProteoFormiX), a voicebot to playfully master a language (LiNGUiNEO), a solution to set up fast and cheap satellite communication in remote areas (KUUB³), a smart waste management solution for cities and GK8’s custodian technology is already helping to securely manage digital assets for clients such as the social trading andmulti asset brokerage company eToro. GK8’s customers manage a total of more than $1 billion in digital assets. The Tel Aviv based startup has completed seed funding round of $4 million, led by Check Point co-founder Marius Nacht and Israel Discount Capital Israeli startup GK8, which provides secure end-to-end management of digital assets, has completed the development of a patented technology for sending transactions to the blockchain without any direct or indirect internet connection. GK8 is now emerging out of

Israeli startup GK8 unveils a technology for sending transactions to the blockchain without internet connection

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cybersecurity in an Israeli intelligence unit.

to criminal hackers or sponsored cyber-attacks. Based on these capabilities, GK8 developed a fully operational end-to-end encrypted system for managing and safeguarding digital assets beyond the reach of hackers. Lior Lamesh, co-founder and CEO of GK8, noted: “Shahar Shamai, GK8’s co-founder and CTO, and I, found security vulnerabilities in one of the most secured cold wallets in the market. After we saw how easy it was, we understood that hackers

GK8 is currently targeting financial institutions, custodians, exchanges and hedge funds which hold cryptocurrencies. GK8’s custodian technology is already operational and helps to securely manage digital assets for clients such as eToro, a global multi-asset trading and investment platform. GK8’s customers manage a total of more than $1 billion in digital assets.

Photo: GK8 co-founders – CEO Lior Lamesh (right) and CTO Shahar Shamai (left). Photo credit Osnat Krasnanski

Current custodian solutions used by financial institutions and state-owned enterprises do not meet institutional scale security standards. In addition to existing security protocols, digital assets based on blockchain technology need a higher security standard since signed transactions are irreversible. Unfortunately, the current blockchain wallet solutions, categorized into “cold wallets” and “hot wallets”, are subject to major cybersecurity vulnerabilities. Cryptocurrency thefts, scams, and fraud worldwide led to the stealing of approximately $4.26 billion in the first six months of 2019 alone. To meet this challenge, GK8 experts have developed proprietary cryptographic techniques that enable real-time blockchain transactions of digital assets without any need for an internet connection. These techniques provide a secure environment to sign blockchain transactions and execute automatic reconciliation confidently. The company’s techniques, protected by five registered patents, can bypass core assumptions related to cryptocurrency transfers and eliminate attack vectors to any asset transfer. These capabilities allowed GK8 to develop the world’s first and only secured cold wallet with hot wallet functionalities. As a result, digital asset transfers secured by GK8 become inaccessible

will invest millions to steal billions, and we decided to develop a secured end-to-end institutional tool for managing digital assets.” Lamesh concluded: “Today, GK8 has a full working solution which is already securing customers who manage around $1 billion for global clients, protected by 5 patents and enjoy the trust and backing of leading cyber-security opinion leaders, partners and investors. This is supplemented by our strong team of cyber- security experts and developers, whose ability to safeguard money is proven, and this has led global organizations to trust our ability to securely manage their digital assets.” “GK8 develops a high security custodian wallet solution, using an exciting and unique approach to cold wallet security,” commented Prof. Eran Tromer, Zcash founding scientist and member of GK8’s advisory board. “Inspired by high assurance critical infrastructure systems, it uses state-of-the-art cryptographic techniques to minimize the wallet’s attack surface and block the influence of a potential attacker on security-critical components.” Eliahu Assif, CISO at eToro, noted: “GK8 provides us with a solution for the entire spectrum of threats, which also includes state sponsored attacks, like the recent attacks we’ve seen on financial industries worldwide.”

Infineon Technologies AG has been again listed in the Dow Jones Sustainability™ World Index. Thus it belongs to the top of the world’s most sustainable companies. Out of 47 companies assessed in the semiconductor sector Infineon is part of the six companies included in the World index. This was announced by the Sustainability Investing specialist RobecoSAM. “It fills us with pride that Infineon has been listed in the Dow Jones Sustainability Index for the tenth time in a row and belongs to the most sustainable companies in the world”, says Dr. Sven Schneider, Chief Financial Officer of Infineon. “To be successful in the long run, business excellence has to go hand in hand with strong environmental and social performance. With its innovative solutions Infineon helps to make more out of less and thus actively contributes to address global challenges like climate change.”, Formula E in Infineon among the top sustainable companies in the world

New-Tech Magazine Europe l 11

Latest News

Paris: Audi drivers brave April weather Created jointly by S&P Dow Jones Indices and RobecoSAM, the DJSI selects the most sustainable companies from across 61 industries. The indices serve as best-in-class benchmarks for investors who integrate sustainability considerations into their portfolios.

lifetimes the reduction of CO 2 emissions by some 56 million tonnes of CO 2 equivalents. This roughly corresponds to the yearly electricity consumption of around 86 million Europeans. Thus, with its products and solutions in combination with efficient production, Infineon achieved a substantial environmental net benefit of

more than 54 million tons of CO 2 equivalents. The CO 2-emissions caused by global product manufacturing are considered in this figure. Infineon creates a substantial ecological net benefit.

Infineon puts a lot of effort into resource-efficient manufacturing globally. Over and above this, the company’s sustainable products and solutions enable throughout their

Ericsson performs novel 5G coverage and performance verification using drone-powered solution from Rohde & Schwarz

Ericsson performs novel 5G coverage and performance verification using drone-powered solution from Rohde & Schwarz Test and measurement specialist Rohde & Schwarz has supplied mobile network testing tools used in drone-based network coverage, performance and operation tests managed by Ericsson, a global leader

has developed together with Centria University of Applied Sciences a novel system for testing cellular mobile network coverage. The new system uses mobile network testing scanners and smartphones from Rohde & Schwarz mounted on a drone that can be programmed to execute automatic tests with considerable flexibility, for

in network infrastructure. The unique procedure enables unprecedented 3D accessibility, positional accuracy and repeatability of the testing. It also opens up new possibilities to ensure end user Quality of Service (QoS) for demanding 5G use cases such as industry 4.0, automotive and public safety. Munich and Kista, September 16, 2019 — The deployment of 5G New Radio (NR) brings new applications of cellular networks for subscribers, government and industry. It also makes the verification of the correct coverage, performance and operation of networks more critical, increasing the demand for accuracy and accessibility in traditional field network tests. A project team based in Jorvas, Finland and led by Ericsson’s 5G Readiness Program RAN Technical Lead Richard Wirén,

example for precise route selection and drone speed control. This solution is especially valuable for industrial use cases. It also has the advantages over traditional walk and drive tests by providing unprecedented repeatability and positional accuracy with the ability to verify beamforming and map coverage in 3D. The R&S TSMA6 network scanner is mounted on a drone and is able to simultaneously verify important LTE and 5G NR coverage metrics such as reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) in accordance with 3GPP standards. When combined with the R&S QualiPoc Android smartphone-based optimizer, IP trace,

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application QoS metrics such as serving cell parameters are possible. The solution currently uses LTE user equipment (UE) but will soon be further developed to include 5G UEs such as the Samsung S10 5G. The drone can be programmed to follow an exact three- dimensional route. The over 20 successful measurement flights conducted so far have shown the solution procedure and results to be extremely repeatable. The drone flights were of various duration, altitudes and routes, depending on the test case. Control, authentication and air traffic control are considerable challenges to the development of robust drone-based solutions. In this new system they are conducted over cellular networks, eliminating the requirement for line-of-sight connection between the drone and its pilot. The project is a collaboration between Ericsson, Rohde & Schwarz, Tampere University and Centria University of Applied Sciences and forms part of the Business Finland 5G FORCE program. Richard Wirén, 5G Readiness Program RAN Technical Lead from

Samsung, SK Hynix, and Micron adding capacity for both DRAM and NAND flash and Intel, Toshiba Memory/Western Digital/SanDisk, and XMC/Yangtze River Storage Technology all significantly ramping up 3D NAND flash capacity over the past 18 months, the DRAM and NAND flash memory markets have entered a period of overcapacity and pricing weakness. This is evident by the steep decline in the price per bit of both DRAM and NAND flash and the steep cutback in capex spending forecast for 2019. In 2019, capital spending in the DRAM and Flash memory segments is expected to drop by 19% and 21%, respectively (Figure 2). Total memory capital spending is expected to be $41.6 billion in 2019, a decline of $10.4 billion from last year. The big cutbacks in spending in the DRAM and flash market segments this year are an attempt by the major memory suppliers to prevent further price erosion in the second half of 2019 and into 2020. How far memory prices continue to fall will be determined in large part by how much memory suppliers trim their capital spending for these devices this year and next and if lower price per bit triggers additional bit volume demand. Ericsson, says, “For 5G to realize its promise, field verification of operation and quality is essential, and this development is a pioneering way to ensure our customers receive the network performance they require. We are delighted to utilize test solutions from Rohde & Schwarz that have proven themselves very reliable and are excited that we now have access to solutions based on commercially available 5G NR UEs such as the Samsung S10 5G.” Vice President of mobile network testing Rohde & Schwarz, Hanspeter Bobst, says, “We are delighted to combine our industry- leading mobile network testing know-how with Ericsson’s long tradition of network innovations to ensure the delivery of end- user Quality of Experience as 5G NR becomes a reality.” Future developments will focus on testing critical 5G applications such as public safety and machine-type communications for Industry 4.0, extending the frequency to extremely high frequencies of the mmWave bands and testing in an urban environment.

Memory Forecast to Account for 43% of Total 2019 Semi Spending

Foundry to account for most semi capex spending in 2019 as the big wave of spending for memory fab expansions and upgrades comes to a close. Capex spending for memory ICs was the driving factor behind very strong increases in industry-wide capex spending over the past two years. Most of those upgrades and expansion plans are now completed

or have entered their final building phases. As a result, memory capex is forecast to account for 43% of total semi industry capital spending this year, down from 49% in 2018 (Figure 1). Total semiconductor capital expenditures are forecast to slip 8% in 2019 to $97.8 billion following the record high spending level of $105.9 billion set in 2018. The share of capital spending for memory devices has increased substantially in seven years, growing from 27% ($14.7 billion) in 2013 to a record high level of 49% ($52.0 billion) in 2018 to a forecast of 43% ($41.6 billion) of total industry capex in 2019, which equates to a 2013-2019 CAGR of 18.9%. The IC product segment receiving the most spending in 2017 and 2018 was the Flash/Non-Volatile memory category. However, with

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Latest News

Vicor appoints European Director of Automotive Business Development

Vicor Corporation today announced the appointment of Nicolas Richard to the role of Director of Automotive Business Development for Europe.

(Continental Automotive), working on projects designing DC-DC converters for hybrid and electric vehicles. Commenting on the appointment, Vicor VP of Sales for EMEA Henryk Dabrowski stated, “I am delighted to have Nicolas join Vicor as we begin projects with several leading OEMs in Europe. Nicolas

Prior to joining Vicor, Nicolas worked at IDT (Renesas) as Automotive Sales and Field Applications Engineer focused on technical sales in powertrain, infotainment and ADAS-based systems. His experience includes numerous engineering roles at On Semiconductor, Continental Automotive and VDO

will lead our initiative to deepen our engagements with these early customers and also expand our design wins to additional automotive Tier-1s and OEMs.”

Volvo Cars Tech Fund invests in Israeli technology start- ups MDGo and UVeye

Volvo Cars has made investments in two promising Israeli technology start-ups through the Volvo Cars Tech Fund, the company’s venture capital investment arm. Both UVeye and MDGo have their headquarters in Tel Aviv, where Volvo Cars has been involved with DRIVE, a so- called ‘accelerator’ for young

car during an accident with medical knowledge with the aim to make automated early and immediate predictions on the type of injuries emergency personnel are likely to encounter at the scene of the accident This data would be transmitted to trauma physicans and emergency personnel via a

companies in the mobility sector, since 2017. UVeye and MDGo have developed their business in recent years with the help of DRIVE, offering the potential to boost quality and safety respectively. They represent the Tech Fund’s first investments outside the US and Europe. MDGo is a company specialising in what it calls medical artificial intelligence. By using advanced machine learning technology it aims to save numerous lives by making sure that people are treated according to their specific injury following an accident with their car. MDGo’s technology will combine real-time data from the

cloud-based platform to improve treatment of the people involved in an accident. As such, the technology has the potential to reduce the likelihood of complications and by extension serious injuries and fatalities. “MDGo’s technology aims to do something that is close to our hearts, which is saving lives,” says Zaki Fasihuddin, CEO of the Volvo Cars Tech Fund. “Their mission as a company seamlessly connects with ours at Volvo Cars, so we are happy to support the continued development of MDGo.” The Tech Fund’s other investment, UVeye, has developed advanced technology for the automatic external

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inspection and scanning of cars for damages, dents and scratches. Volvo Cars is not just investing in the company, but is also looking at using UVeye’s technology for conducting full exterior inspection of cars after they roll out from production lines. Volvo Cars believes using UVeye’s technology could further improve the quality of cars leaving the factory and ensure that even tiny faults are detected. A first pilot is intended to start later this year at its manufacturing plant in Torslanda, Sweden. The technology could also be used during the various steps of the logistics flow and at dealerships. “Premium quality standards are at the core of the Volvo brand and we are intrigued by the possibilities that UVeye’s technology offers,” Zaki Fasihuddin says. “This type of advanced scanning technology could allow us to take the

fine ranging technology that delivers precise outdoor and indoor localization. UWB meets these requirements and is a clear enhancement compared to existing wireless technologies such as Wi-Fi, Bluetooth and GPS. Its ability to process contextual information such as the position of the UWB anchor, its movements, and distance to other devices with an unprecedented precision of a few centimeters in real- time, will enable a host of new and exciting applications. Lars Reger, Chief Technology Officer, NXP: “We see enormous potential in UWB. As a co-founder of the FIRA Consortium we are working to enhance the technology, drive its standardization and also to develop new use cases. A potential application, that I personally find very compelling, is the potential UWB has to replace the key ring for your home, office or car.” Maik Rohde, head of body electronics and car access, Volkswagen: “The first UWB application we see is in theft protection – another security milestone which you will see in volume Volkswagen car models starting this year. But this is only the beginning. UWB, especially when combined with high-precision sensors and Artificial Intelligence, can deliver further benefits. Some of these you can experience in our concept car.” next step in quality.” The Volvo Cars Tech Fund was launched last year and invests in high-potential technology start-ups around the globe. It focuses its investments on strategic technology trends transforming the auto industry, such as artificial intelligence, electrification, autonomous driving and digital mobility services. Since 2018 the Tech Fund has invested in a number of companies, including Luminar Technologies, a leading firm in the development of advanced sensor technology for autonomous cars; Varjo, a maker of high-end augmented reality headsets; and Zum, a ride sharing service for children. Other investments include electric car charging firm Freewire and Forciot, which develops connected, printable and stretchable electronics.

NXP and VW share the wide possibilities of Ultra-Wideband’s (UWB) fine ranging capabilities

NXP Semiconductors N.V. and Volkswagen have shared early glimpses into Ultra-Wideband (UWB) technology and its future applications. In a Volkswagen concept car, the companies showed the capabilities of Ultra- Wideband for advancing security, safety and convenience in vehicles. Lars Reger, CTO of NXP Semiconductors, and Maik Rohde,

head of body electronics and car access systems, Volkswagen, discussed the pioneering collaboration as part of a broader, cross-industry push to leverage the unique capabilities of UWB: accurate localization and fine ranging at maximum security levels. In automotive alone, UWB will enable interesting new use cases such as automated trailer hitch activation, in-cabin passenger detection, automated valet parking, hands-free parking, lot access and drive-through payment, to name a few. Another interesting application is walking pattern recognition for car access, which was demonstrated in the VW concept car. The Volkswagen UWB car key used high-precision sensing technology and Artificial Intelligence to learn personalized user gestures. Developers of groundbreaking applications in a broad range of markets including mobile, automotive, Internet-of-Things (IoT) and the Industrial space, have been actively seeking a secure,

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IMUs: Let Your Host Sleep with On-Board Machine Learning

Rich Miron, Applications Engineer at Digi-Key Electronics processing from the host application processor and how these features can be used in real applications. A quick IMU review

Inertial measurement units (IMUs) are widely used to provide a steady stream of multi-axis position information from accelerometers, gyroscopes, and other sensors. With the many degrees of freedom (DOF) all generating data, the merged data streams from these devices can keep system processors in constant wake mode and tax them as they sift through the raw IMU data to extract useful gesture and system location information. What designers need is a way to offload this sifting function from the main processor. Machine learning may be the answer. After a brief overview of IMU use, this article introduces the 6DOF LSM6DSO from STMicroelectronics. It then uses this device to show how the addition and integration of machine learning and decision tree processing into IMUs can offload real-time position and movement

bias or integration result in a position error called “drift,” which can be compensated for with software. Accelerometers measure linear acceleration, including acceleration components caused by device motion and acceleration due to gravity. The acceleration unit of measurement is g, where 1 g = the earth’s gravitational force = 9.8 meters/second2. Accelerometers are available with one, two, or three axes, which define an X, Y, Z coordinate system. Magnetic sensors measure magnetic field strength, typically in units of microTeslas (µT) or Gauss (100 µT = 1 Gauss). The most common magnetic sensor used for mobile electronics is a three-axis Hall effect magnetometer. By computing the angle of the detected earth’s magnetic field, and comparing that measured angle to gravity as measured by an accelerometer, it

IMUs integrate a number of motion sensors into one device and can provide high accuracy positioning information. They can be used for a variety of applications including consumer (mobile phones), medical (imaging), industrial (robotics), and military (head tracking). They react to the motion of the sensor and incorporate one or more of the following motion sensor types: Gyroscope sensors measure angular position changes, usually expressed in degrees per second. Integrating angular rate over time results in a measured angle of travel that can be used to track changes in orientation. Gyroscopes track relative movement independently from gravity, so errors from sensor

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accelerometer data is therefore filtered out in the short term and smoothed by the gyroscope data. The computational horsepower needed to perform all of this sensor processing, filtering, and fusing consumes energy, which can be a problem in battery-powered systems, especially when the IMU information is not needed as a continuous stream. For many embedded applications, significant power savings can be realized if the IMU can generate an interrupt that awakens the host processor from sleep mode so that it can initiate processing or take some action as a result of the interrupt. To enable this capability, some IMU vendors are starting to incorporate processing and decision making features in their IMUs. Let the IMU do the thinking The 6DOF LSM6DSO from STMicroelectronics is one such IMU. It incorporates three microelectromechanical systems (MEMS) gyroscopes and three MEMS accelerometers and can detect orientation changes and gestures without oversight or assistance from a host processor, all using on board processing. The IMU consumes 0.55 milliamps (mA) running in its highest performance mode. In this mode, the LSM6DSO can continuously monitor its own attitude and movement in space and can generate an interrupt upon a prearranged condition that awakens the host processor to perform additional processing on the sensor stream. Using a low- power IMU that can always remain operational is beneficial because it lets the host processor sleep, awakening it only when necessary.

is possible to measure a device’s heading with respect to magnetic north with high accuracy. Motion tracking using IMUs employs sensor fusion to derive a single, high accuracy estimate of relative device orientation and position from a known starting point and orientation. Sensor fusion usually employs software to combine the IMU’s various motion sensor outputs using complex mathematical algorithms developed either by the IMU manufacturer or the application developer. Position calculations using sensor fusion can produce the following measurements: Gravity – specifically the earth’s gravity, which excludes the acceleration caused by the motion being experienced by the device. An accelerometer measures the gravity vector when the IMU is stationary. When the IMU is in motion, the gravity measurement requires fusing data from an accelerometer and a gyroscope and subtracting out the acceleration caused by motion. Linear acceleration – equivalent to the acceleration of the device as measured by the accelerometer, but with the gravity vector subtracted using software. IMU linear acceleration can be used to measure movement in three- dimensional space. Orientation (attitude) – the set of Euler angles including yaw (azimuth), pitch, and roll, as measured in units of degrees. Rotation vector – derived from a combination of data from accelerometer, gyroscope, and magnetometer sensors. The rotation vector represents a rotation angle around a specified axis. Sources of IMU error Gyroscopes sense orientation

through angular velocity changes, but they tend to drift over time because they only sense changes and have no fixed frame of reference. Adding accelerometer data to the gyroscope data allows software to minimize gyroscope bias for a more accurate location estimate. Accelerometers sense changes in direction with respect to gravity, and that data can be used to orient a gyroscope. Accelerometers are more accurate for static (as opposed to dynamic) calculations. Gyroscopes are better at detecting orientation when the system is already in motion. Accelerometers react quickly, so accelerometer jitter and noise produce accumulated error when that data is used alone. Additionally, accelerometers tend to distort accelerations due to external forces such as gravitational forces, which also accumulate in the system as noise. Filtering this data improves accuracy. Combining a gyroscope’s short-term accuracy with an accelerometer’s long-term accuracy results in more precise orientation readings by relying on each sensor’s strengths to cancel or at least reduce the other sensor’s weaknesses. The two sensor types complement each other to help reduce errors, but there are other ways by which errors are reduced. Fused filtering needed to reduce error IMU software uses filtering to minimize positioning error from IMU data. Several filtering methods for fusing sensor data are available, each with varying degrees of complexity. A complementary filter combines a high pass gyroscope filter and low pass accelerometer filter. High frequency noise in the

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This is a tried and trusted means of saving energy in battery-powered systems. In addition to its gyroscope and accelerometer sensors, the LSM6DSO IMU contains a signal conditioning and filter block, a finite state machine (FSM) that can run as many as 16 programs —all sharing a common, configurable output data rate—and a machine learning core. Used together, these resources can generate event detection interrupts for the following conditions: Free fall Wakeup 6DOF orientation Single-click and double-click sensing Activity/inactivity recognition Stationary/motion detection The signal conditioning block applies conversion factors stored in its sensitivity registers to scale the raw sensor data. It then converts the raw IMU sensor data stream into a 16-bit, half-precision floating point (HFP) byte format that the FSM can understand. The IMU’s MEMS sensors (the accelerometers and gyroscopes) along with the two analog-to-digital converters (ADCs) and four filter blocks are shown in Figure 1. The filter blocks are used to convert the analog MEMS sensor signals into filtered digital data streams. The programmable FSM consists of a configuration block and sixteen program blocks. The FSM’s configuration block configures and controls the entire FSM. Each of the FSM’s sixteen program blocks consists of an input selector block and a code block (Figure 2). Both of these blocks are controlled by values written to registers within the IMU. The Input Selector block routes the selected input data from one of the IMU’s internal sensors or from

Figure 1: The LSM6DSO IMU uses two ADCs to convert analog signals from its internal MEMS accelerometers and gyroscopes into digital streams. The ADCs are followed by four digital filters to condition the signals for decision making by the internal FSM and machine learning core and by the host processor. (Image source: STMicroelectronics)

Figure 2: Each of the FSM’s sixteen program blocks in the LSM6DSO IMU consists of an input selector block and a code block. (Image source: STMicroelectronics)

portion of the code block’s memory footprint, a programmable reset vector, and a program counter. Because these are all 8-bit values, each FSM program is limited to 256 bytes. The program block’s instruction section contains the actual FSM program. Program instructions include opcodes for checking sensor inputs against thresholds, checking for zero crossings, and checking timer values for timeout comparisons. The opcodes specify the condition needed to pass from the current FSM state to the next. In addition, there are command opcodes for selecting thresholds and masks stored in the program’s variable data section; for setting the IMU’s sensor hub multiplex selector to connect to one of the four possible external sensors; and

an external sensor connected to the IMU’s sensor hub to the code block. The IMU’s sensor hub can accommodate as many as four additional external sensors such as magnetometers, which are connected to the IMU via an I2C port. The FSM’s code block contains one program for the state machine. The program block’s data section’s fixed portion consists of six bytes that define the number of thresholds, hysteresis, mask, and timer settings for the program. The program block’s variable data section holds the actual threshold, hysteresis, mask, and timer settings for each program as defined by the values stored in the fixed part of the data section. The fixed portion of the data section also defines the size of the variable

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for asserting an interrupt. Each FSM program can generate an interrupt and can modify the contents of a corresponding register value based on the selected input signal. These register values are used to pass data from the IMU to the host processor. It’s useful to think of the FSM as a microprocessor minus the arithmetic logic unit. The FSM can make selections, perform comparisons, and can make decisions about its next state based on those comparisons. It does not compute values other than the Boolean results from the comparisons. The FSM is not a microprocessor. It can make comparisons and perform simple changes to the program flow based on these comparisons. Because it’s a simple machine, it’s programmed directly with the FSM opcodes. There is no high level language compiler for the FSM, but the programs are generally so simple that no compiler is needed. Using the FSM The LSM6DSO IMU’s FSM can be programmed to generate interrupt signals activated by predefined motion patterns. The FSM can run as many as 16 simultaneous, independent programs to detect motion. Each FSM program consists of a sequence of if-then-else steps and uses the sensor streams from the LSM6DSO’s accelerometers and gyroscopes as inputs. If any of the FSM programs detects a match to its preprogrammed pattern, the FSM can generate an interrupt to the host processor. Each of the sixteen possible FSM programs contains three memory sections for fixed data, variable data, and instructions. A single FSM program block diagram is shown in Figure 3.

Figure 3: The FSM in the STMicroelectronics LSM6DSO IMU incorporates sixteen code blocks, each of which contains three memory sections for fixed data, variable data, and instructions. (Image source: STMicroelectronics)

demonstrate the use of the various FSM features. Any of these sample programs can be installed into an IMU demo platform like the STEVAL-MKI109V3 eMotion STM32 eval board, which has a 28-pin socket that accepts the LSM6DSO STEVAL-MKI197V1 IMU adapter board. Programming the STEVAL- MKI109V2 board with one of the example programs requires only a few clicks in the Unico development environment. However, there’s a bit more to the LSM6DSO than meets the eye. The machine learning core The LSM6DSO IMU also incorporates a more sophisticated and programmable pattern matching engine called the machine learning core. This can identify classes of movement using the multiple sensor data streams from the internal IMU sensors and any attached external sensors. Recognizable event classes include stationary (no movement), walking, jogging, biking, and driving. Classification takes the form of decision trees within the machine learning core. The machine learning core consists of three blocks: the sensor data

The structure of a single program in a code block consists of three sections in a block of memory: A fixed data section, which has the same size for all the FSM programs A variable data section, which can vary in size An instruction section, which contains conditions and commands Programming each FSM code block involves loading the three memory sections with programing values that determine the FSM’s behavior. STMicroelectronics provides an FSM programming tool within its downloadable Unico evaluation development software and development environment. STMicroelectronics has also included several sample FSM programs with the Unico development tools as an aid in learning how to program the FSM. These sample programs demonstrate several IMU based interrupt scenarios including: A basic pedometer System in free fall Simple motion detection System has been picked up System has been shaken System has stopped moving (stationary) Wrist tilt The sample FSM program examples

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block, the computation block, and the decision tree (Figure 4). The machine learning core’s sensor data block aggregates data streams from the IMU’s internal accelerometers and gyroscopes and from any external sensors attached to the IMU through the I2C interface. The computation block can filter the sensor data using predefined filtering parameters, and compute windowed statistics including the mean, variance, peak-to-peak amplitude, minimum, maximum, and zero crossing for the sensor data. The decision tree compares the computed sensor data statistics against thresholds to classify the input data. As with the LSM6DSO’s FSM, a dedicated tool in the Unico development environment is used to program the IMU’s machine learning core. The finite state machine and the machine learning core can also be used in conjunction with a host processor to implement more sophisticated position tracking algorithms. The downloadable STMicroelectronics X-CUBE-MEMS1 software pack for the company’s STM32Cube development system includes the following example software routines: Activity recognition – Provides information on the type of activity being performed by the user including holding still, walking, fast walking, jogging, biking, or driving. This algorithm might typically be used in a mobile phone or some sort of wearable device. Motion duration detection – When combined with pedometer data, motion duration detection can be used to determine the number of seconds that a user is active. This algorithm might typically be used in a wearable device for fitness or health tracking.

Figure 4: The machine learning core in the STMicroelectronics LSM6DSO IMU consists of three blocks: a sensor data block that aggregates data streams from internal and external sensors, a computation block that filters the sensor data and computes statistics on that sensor data, and a decision tree that classifies events based on the computed statistics. (Image source: STMicroelectronics)

Vibration or motion intensity detection – Provides information about the intensity of user motion and can distinguish motion intensity in a range from 0 (still) to 10 (sprinting). This algorithm might typically be used in a mobile phone or some sort of wearable fitness device. Carrying position recognition – Provides information about how the user is carrying a device and can distinguish among the following positions: on a desk, in a hand, near the head, in a shirt pocket, in a trouser pocket, in a jacket pocket, and held in a swinging arm. This algorithm might typically be used in a mobile phone or some other sort of carried device for activity related context detection. Conclusion The need to keep a host processor running tomaintain a position fix and to detect movement and gestures from IMU data can be a difficult goal to achieve with battery-powered embedded designs because of the host processor’s relatively high power consumption. However, a

new generation of low-power IMUs with sufficient on board processing to perform machine learning can solve this problem by allowing the host processor to sleep in a low current mode until it’s needed. About this author Rich Miron, Applications Engineer at Digi-Key Electronics, has been in the Technical Content group since 2007 with primary responsibility for writing and editing articles, blogs and Product Training Modules. Prior to Digi-Key, he tested and qualified instrumentation and control systems for nuclear submarines. Rich holds a degree in electrical and electronics engineering from North Dakota State University in Fargo, ND.

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