IIoT

The state of manufacturing is changing rapidly. Regardless of sector or location, manufacturing decision-makers across the world are signaling a desire for better supply chain resiliency, manufacturing flexibility, increased speed of innovation and stronger environmental sustainability. Singapore’s manufacturing sector, a significant contributor to its gross domestic product, is always evolving and today is shifting away from its traditional focus on producing highly customized products using flexible manufacturing processes, but at significantly lower efficiencies. Today, with Industry 4.0, we can design manufacturing systems that optimize both efficiency and flexibility. And this is possible because of the convergence of technologies such as artificial intelligence (AI), data analytics, robotics and the Industrial Internet of Things (IIoT). This blend of technologies helps reduce the cost of technological solution ownership – a derivative of Right’s Law – as a function of cumulative production. In HP Singapore, driving innovation in our product and processes is part of our DNA, and over time our products have grown in complexity and breadth. We have embraced Fourth Industrial Revolution (4IR) technologies in our advanced manufacturing lines. We started our Industry 4.0 journey in 2016 with Vision and Mission 2020 to modernize our production facilities to smart factories that strengthen our competitive edge. Our focus was on upskilling our employees with future skill sets, build new technological capabilities and partner with higher education institutes. To drive these transformations, we have formulated five pillars: Additive Manufacturing Data Analytics Cyber-Physical Integration Digitalization Workforce Transformation These five pillars have enabled us to move from labor-intensive and reactive processes to processes that are highly digitized, automated, and AI-driven, enabling us not only to increase quality and productivity but also to reskill our people in anticipation of jobs they will need in the future. Technicians have been upskilled and promoted to techno-operators which has, in turn, freed up technical specialists to explore other roles. Engineers have retrained as data scientists, or have moved to new product development, for instance. In 2017, HP’s Ink Supplies Operations (ISO) set up Smart Manufacturing Applications and Research Centre (SMARC) to adopt 4IR technologies and implement these innovations in production lines. Today, SMARC is the home ground for HP engineers to experience, trial and prototype solutions, bringing innovative and sometimes unexpected solutions to manufacturing. It is also a showcase for industry partners, government agencies and schools. Here is how each pillar of the SMARC contributed to transformation to augment the manufacturing workforce: Cyber-Physical Integration – Move Role of robotics/automation – By standardizing automation standards for robotics, we have deployed collaborative robots (Cobots) and autonomous intelligent vehicles (AIVs) to perform manual and routine tasks to drive productivity, while reducing errors from operator fatigue and protecting our operators’ physical well-being. Digitalization – Sense Role of IIoT – Devices are a treasure trove of data that can provide clarity on how the entire manufacturing line is performing in real time. Building a platform that connects devices and collects data while allowing factory floor managers to dynamically visualize on an Integrated Command Centre (ICC) and manage factory performance is central to HP’s digital transformation journey. And IIoT is not restricted to just devices that are already wired for data sharing. HP has also connected off-the-shelf analogue devices using a standardized data transportation protocol, allowing HP to collect essential data across all types of devices and eliminating manual data entry. Additive Manufacturing – Build By embracing additive manufacturing (use of HP MultiJet Fusion 3D printers), HP introduced more flexibility in operations through on-site rapid prototyping, light production, and replacement of parts needed on our manufacturing floors, shortening production timelines. We 3D printed pallets, which are cheaper and faster to produce, and replaced original pallets for transportation on conveyor belts, improving the efficiency and productivity of our operators. Director Jamie Neo with HP’s MultiJet Additive Manufacturing Printer. (Photo Credit: HP) The HP Multi Jet Fusion 3D printing technology has helped HP to replace traditional manufacturing methods and streamline processes in our supply chain. For example, HP is 3D printing the Drill Extraction Shoe, a tool that is essential to the removal of waste products from laser-drilling in HP’s printhead manufacturing line. Through 3D printing, HP has consolidated the production of the tool from nine parts to one 3D printed model, thereby optimizing the design of the tool and reducing its production time from three to five days to 24 hours. Data Analytics – Think By deploying advanced analytics and machine learning models, HP has enabled real-time detection, diagnostics, and prediction of product quality across our manufacturing lines. Predictive models are replacing traditional “destructive testing,” reducing waste and allowing HP to meet unique product specifications more accurately. Machine learning is diagnosing and recommending the right set up for tools and manufacturing lines, when necessary, to reduce downtime and increase precision. Workforce Transformation – Grow The pivot to becoming an advanced manufacturing leader not only requires HP to invest in 4IR technologies but also skill sets to operate 4IR technologies. We embarked on a Workforce Transformation program to help our employees stay competitive in a fast-changing world. Today 35% of HP technical workforce have had the opportunity to take on new roles even as needs evolve, thanks to internal and external training and reskilling. Beyond technology and training, the glue that binds these together and makes it successful is our culture at HP. We are ambition-led, which means that we do not see the world as it is, but what we can be. And we do so by collaboration. Plans for the Future After accomplishing our Mission 2020, in late 2020 we launched Mission 2025 to extend our end-to-end smart factory capabilities through advanced connectivity, intelligence and automation to optimize and drive sustainable manufacturing flexibility and efficiency. Pyramid of HP’s smart manufacturing focus Advanced technologies such as additive manufacturing, IIoT, automation and robotics, data analytics, machine learning and AI are central to the connectivity and the end-to-end intelligence of our smart factories, enhancing production efficiency and flexibility while improving the quality of our products. For example, the deployment of IIoT sensors in our wafer plant has helped to reduce downtime in replacing CO2 gas cylinders. What’s more, AI enables us to more accurately monitor the dispensing of structural adhesive to eliminate lost yield. We believe that by enhancing manufacturing efficiency and flexibility, we were able to shorten resolution time, reduce our carbon footprint, and improve the resiliency of our manufacturing and supply chain systems. HP smart factory model In April 2021, two lines in HP Singapore joined the World Economic Forum’s Global Lighthouse Network after being recognized for pivoting from a labor-intensive factory into a digitized, automated one with the help of AI. In doing so, we managed to improve manufacturing costs by 20% and productivity by 70%. Under Mission 2020, we saw the following successes: Improved manufacturing costs by 20% Improved productivity by 70% Brought most HP employees onboard to our smart manufacturing journey Equipped HP employees with skill sets in areas such as additive manufacturing, data analytics, AI, robotics and Internet of Things Established a Model Factory playbook With Mission 2025, we will: Continue to train employees in future skillsets by partnering with institutes of higher learning Scale our Model Factory playbook across more manufacturing lines to reduce costs and improve productivity Enhance our knowledge in additive manufacturing by building an ecosystem as a service platform to help manufacturing companies Enable a sustainable manufacturing system to reduce our carbon footprint and help enable a circular economy We believe in innovating with purpose by focusing on solving real-world problems and creating technology in the service of humanity. That is why we built the SMARC to create the solutions for our lines and showcase these solutions to encourage industry participation. We are driven by values and ambition, which means that it is not just what we do, but also how we execute it. We make sure our values inform everything we do – for instance, helping us make a greater impact to environmental sustainability, people, and our community. We believe this is a crucial step in coalescing industry support, which is necessary to move the needle on advanced manufacturing. Robert Ronald is Master Program Manager, Cost Structure, Model Smart Factory and Sustainability, at HP.

Ride the Wave of Smarter Manufacturing The year 2020 sparked a tremendous acceleration in the digital transformation worldwide, driving a sharp rise in demand for semiconductors and escalating pressure on chip factories to reduce manual functions on the shop floor. The mindset of the semiconductor industry saw a remarkable shift as it recognized with heightened urgency the need to deploy data-driven visualization, analysis, scheduling and dispatching solutions to increase automation to improve production speed and efficiency. Amidst the new excitement around Industry 4.0, chip manufacturers are rapidly deploying new technologies including IIoT, big data, machine learning and Autonomous Intelligent Vehicles (AIVs). Yet for many chip manufacturers, the path to building a smart factory is far from clear because they lack an overall digital transformation strategy. Smart manufacturing is a broad concept covering an array of technologies and solutions, making a holistic, mid- to long-term digitalization strategy rooted in the overall business strategy crucial. There are no shortcuts that can move a manufacturer instantly to Industry 4.0. Instead, this transformation is a step-by-step undertaking with a natural evolution. Some Factory Tasks Must Remain Manual – For Now The semiconductor industry has reached a point where manual processes are no longer efficient enough to support mass chip customization and remote operations. The many technological and standardization advances behind automation can help streamline some of a factory’s most labor-intensive tasks including the loading or unloading of machines or lot tracking and data collection while reducing operational costs. Still, some tasks remain very difficult to automate. For example, handling errors and exceptions presents the greatest challenge since some errors are hard to anticipate. What’s more, the cost of automating error handling can be prohibitive. Eliminating Gaps in Connectivity Often, critical data sources aren’t available due to lack of equipment integration, incomplete product quality monitoring or gaps in material tracking. Closing these gaps in connectivity enables the collection of data and provides rich, reliable information for analysis and reporting that can drive continuous operational improvements, optimizations and efficiencies throughout a factory. But keep in mind that data integration alone can be a challenging task. The selection and proper enrichment of relevant data is, in many cases, not just a technical problem but requires a detailed and in-depth knowledge of the manufacturing steps to be analyzed and optimized. Even when data is available, it might be still difficult to make decisions or implement improvements if it is in siloed systems that require manual processes to integrate and translate into useful information. Problem solving at this level is possible but extremely time-consuming. Manual integration is not only ineffective but costly, draining time, human resources and money from the factory. The right contextual information for the data is vital to unleash its potential and make improvements possible. Dispersed solutions cannot control processes because they span functional areas and people, physical and business entities. Backbone software for shop-floor operations that controls all other applications is central to smart manufacturing. Data-Driven Manufacturing The semiconductor industry is expert in data collection and leads many other industries in this area. The problem is often that chip companies use only a fraction of the information they collect for the analysis and insights needed to improve operational efficiency. By comprehensively integrating all distributed data into a single version of truth – in one location where it is always available – companies can make data analysis and problem solving almost frictionless. Keep in mind that data platforms and edge solutions, within the context of manufacturing, will not be adopted as part of a greenfield initiative. Building a solid automation architecture is only feasible and beneficial by deploying new technologies such as machine learning and artificial intelligence (AI). Analysis of historical data provides important context and reveals deviations such as unexpected process time, uncommon material accumulations or issues with material transport. By integrating swift control actions for new data point collected, manufacturing operations can shift from reactive problem-solving to proactive analysis and operational improvements. The tremendous increase in interest and investment in AI for manufacturing automation only became possible with the availability of low-cost sensors that generate huge volumes of data and solutions for storing and processing that at low cost. AI and other leading-edge technologies transform the tedious but critical process of extracting insights from data, making it instantaneous, streamlined and achievable for every manufacturer. The maturity of smart manufacturing hinges on the extent to which a factory is data-driven. This requires foundational investments to improve traceability, connectivity and real-time operations – and finally making sure that data helps us what to do and when to do it. Ricco WALTER is managing director of SYSTEMA Automation in Singapore.

The microelectronics industry is entering the era of Cloud Engineering Simulation to slash the costs and risks of new technology development and speed time-to-market in spaces like semiconductors, MEMS sensors, RF front ends, biomedical and driverless cars. In the run-up to SEMICON Europa, 12-15 November, 2019, in Munich, Germany, SEMI spoke with Ian Campbell, CEO of OnScale, about the new paradigm of Cloud Engineering Simulation. Campbell shared his views ahead of the SMART Design Forum, 14 November, 2019, 14:30 to 17:00, in Hall B1, TechARENA 1 at SEMICON Europa. Registration is open. Join the forum to meet experts from OnScale and other key industry influencers. Attendance is free of charge for all SEMICON Europa visitors.SEMI: How did your adventure with OnScale start?Campbell: I’m an engineer. When I was still in high school, I took a night class at Nashville Tech to learn AutoCAD R14, and I’ve been designing and engineering things ever since. I was introduced to Desktop Simulation in my bachelors of mechanical engineering program and used many types of simulation tools for massive design studies at the Aerospace Systems Design Lab at Georgia Tech. I’m a simulation junkie.I started my first Silicon Valley high-tech company, NextInput, in 2012 with Dr. Ryan Diestelhorst (now VP of Strategy at OnScale), to commercialize new ForceTouch and 3D Touch technologies based on our patented MEMS force sensors. At NextInput, we bought hundreds of thousands of dollars of engineering software, but were always frustrated by slow, inaccurate engineering simulation results. We dreamed about running massive simulations on Cloud Supercomputers and creating true Digital Prototypes that could replace costly, time-consuming, and risky physical prototypes.When I got the chance to join the team that became OnScale in 2017, I jumped at the opportunity. At OnScale, we took engineering simulation solvers that had been developed for the U.S. military to run on U.S. Department of Defense and DARPA supercomputers and built a cloud supercomputer platform on Amazon Web Services to run the solvers. The net-net is the world’s first on-demand, infinitely scalable Cloud Engineering Simulation platform. Now, we routinely run massive multi-billion degree of freedom simulations for Fortune 100 companies, including many from the semiconductor and MEMS industries. Since our business model is to charge per core-hour for simulations, the incredible capability we built is cost-effective and available to small startups as well. SEMI: How is the semiconductor design ecosystem evolving? How is Cloud Engineering Simulation applied to semiconductor and design industries?Campbell: The entire industry is experiencing a massive acceleration in product launch cycles and increased competition. New markets like IoT and 5G are reducing semi/MEMS product cycles from years to months. That, in turn, puts enormous pressure on semiconductor and MEMS designers. Missing a key product introduction like a flagship smartphone launch can literally make or break a company.A reliance on traditional engineering methods – schematic capture and layout of a chip, taping out (physically prototyping the chip), performing engineering validation on an e-bench, qualifying the chip (or not qualifying it and going back to the drawing board), and finally launching mass production – is no longer sustainable from a competitive perspective.Instead, market-leading firms are turning to Cloud Engineering Simulation and Digital Prototypes to explore massive design spaces, find optimum designs that beat the competition in every KPI (size, power, performance), and digitally qualify designs before ever cutting silicon, ensuring that designs are robust over their intended operating environments and performance envelopes. Large thermal analysis of a chip on a circuit board executed quickly on the OnScale Cloud Simulation Platform SEMI: Can you give us an example? Campbell: A great example is thermal analysis. Thermal effects have always had huge impacts on MEMS device performance and, more recently, they are beginning to impact performance of next-gen semiconductors, especially GaN power electronics for electric vehicles (EVs).Conducting a full system-level thermal analysis of something like an EV power management system – a power IC in a package, on a board, in an enclosure, under various loading conditions – has been a challenge from a simulation complexity perspective (degrees of freedom) and from a parametric sweep perspective (running hundreds or thousands of simulations to optimize chip placement, routing, etc.). To run these sets of simulations using legacy desktop simulation would take weeks, perhaps even a month or more. To run these massive simulations in parallel on cloud supercomputers using OnScale takes days or even hours.Our customers routinely run very large simulation studies on OnScale Cloud for thermal simulations, RF filter simulations, MEMS simulations, packaging simulations (what we call Digital Qualification), and many more use cases.SEMI: What’s one of your strategic objectives for 2020? Campbell: For 2020, we’re doubling down on MEMS and semi simulation capabilities. We will be launching additional solver capabilities like EM that will be critical in our strategic markets like 5G. We will also be launching a Cloud API so that engineers can integrate OnScale directly into their existing engineering workflows (e.g. MATLAB or EDA/CAD tools) with just a few Python commands.SEMI: Can you share one prediction for the future of semiconductor design solutions? share?Campbell: I think we will continue to see MEMS and semi designers push the envelope and bring smaller, more performant, more cost-effective solutions to market. I’d like to see more highly cost-effective flexible semi/MEMS designs come to market to enable next-gen IoT and IIoT applications. I’d also like to see more biomedical applications – biomems, microfluidics, and labs on a chip for all sorts of life-enhancing applications.SEMI: What are your expectations regarding the SMART Design Forum at SEMICON Europa 2019 in Munich? Campbell: I’m looking forward to getting back to my roots in MEMS/semi design and chatting with other designers about the future of engineering and the future of semi! Ian Campbell is a twice venture-backed Silicon Valley CEO and expert in MEMS sensors, semiconductor technology, and engineering software. Most recently, Ian co-founded OnScale, a Cloud Engineering Simulation startup backed by Intel Capital and Google’s Gradient Ventures. OnScale is revolutionizing engineering by combining world-class multiphysics solvers with Cloud supercomputers, machine learning, and artificial intelligence. Prior to co-founding OnScale, Campbell served as founder and CEO of NextInput, where he led the startup through multiple rounds of funding – totaling $12 million and an additional $4 million in research contracts with government and industry partners – and built a world-class team of engineers and scientists who developed 3D Touch and ForceTouch technologies for smartphones, wearables, industrial, and automotive interface applications. He also secured the first major smartphone OEM design wins in Asia. Campbell earned his B.S. in mechanical engineering from Middle Tennessee State University, and his MSAE in aerospace engineering and MBA from Georgia Institute of Technology.Serena Brischetto is senior manager, marketing and communications, at SEMI Europe.

Artificial intelligence (AI) is on the verge of transforming entire industries as it gears up to power semiconductor industry innovation and growth, thrusting the technology to front and center at SEMICON Japan 2019, December 12-14 at the Tokyo Big Sight (Tokyo International Exhibition Center).The SMART Technology Forum at SEMICON Japan will highlight the latest AI developments and trends. Supported by U.S. Commercial Service in Japan, the forum will feature Yutaka Matsuo of the University of Tokyo. An authority on AI, Matuso will give an overview of both AI business and technology. His presentation will be followed by an AI outlook from Microsoft Japan, Amazon Web Services and DefinedCrowd.A number of Japanese startups are on leading edge of AI innovation in machine and deep learning. One is Preferred Networks Inc., a company that applies cutting-edge deep learning technology to Internet of Things (IoT) applications across transportation, manufacturing and healthcare.In his opening day keynote at SEMICON Japan, Toru Nishikawa, president and CEO of Preferred Networks, Inc., will highlight the latest developments and promise of using deep learning for industrial applications. Nishikawa will unpack how AI companies jockeying for competitive advantage will win by harnessing technologies to process massive amounts of data efficiently and quickly.Following is look at Preferred Networks, Inc. and five other Japanese startups that are driving AI innovation. Within Japan's world of AI, machine learning, and deep dearning, Preferred Networks is likely the most well-known Japanese company. The parent company, Preferred Infrastructure, was founded in March 2006 by Toru Nishikawa and Daisuke Okanohara, who focused on search engine development before turning to machine learning and establishing Preferred Networks to commercialize the technology.Preferred Networks established itself as one of the world’s top providers of machine learning technology with the development of Chainer – an open source deep learning framework that has been offered free of charge since June 2015 and was released before TensorFlow, Google’s renowned Deep Learning framework. Established in 2012, ABEJA is thought to be Japan’s first venture company to specialize in deep learning. ABEJA's core technology is its AI platform ABEJA Platform. Based on this platform, the company offers various solutions to more than 100 client companies. ABEJA also offers ABEJA Insight, a specialized package service for the retail and distribution, manufacturing, and infrastructure industries. Data analytics provider BrainPad Inc. was the first Japanese AI venture listed on the Tokyo Stock Exchange. Established in 2004, before the advent of big data, BrainPad Inc. cultivated a vision of analyzing vast amounts of data in increase the competitiveness of Japanese companies. LeapMind Inc. aims to offer deep learning technology that uses fewer computing resources and draws less power. Both are important capabilities since deep learning requires considerable computing resources to perform image and speech recognition. The company’s answer to this deep learning challenge is a small form factor FPGA with low power consumption.In April 2018, LeapMind started offering the tool DeLTA-Lite to support model construction for Deep Learning. The tool simplifies the development of deep learning design models, eliminating the need for model design, hardware, and software expertise. Hacarus Inc.’s HACARUS-X AI technology, which combines sparse modeling and machine learning technology, features low power consumption and small devices such as FPGAs. In collaboration with semiconductor trading company PALTEK, Hacarus is integrating HACARUS-X algorithms with Xilinx's FPGA Zynq UltraScale + MPSoC. Both companies area also implementing HACARUS-X algorithms in a box computer.Sparse modeling is gaining attention as a modeling method by which humans can understand the judgment process of AI by extracting features from a small amount of learning data. With expertise in life science fields such as medical and biology and image processing technology, LPixel, Inc. develops image analysis systems with original algorithms and machine learning techniques. It has developed a cloud-based AI image analysis platform and an AI medical image diagnosis support technology that streamlines the review of large amounts of research data and detects image fraud in research papers and other documents for the medical and biology fields, freeing researchers to devote more time to their core work. Yoichiro Ando is a marketing director at SEMI Japan.

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.

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!