blockchain

Part 2 of 2-part series on MSEC 2019 highlights. Read Part 1. Neural Networks on ChipTo be sure, low power is king when bringing machine learning to the sensor edge. Battery-powered, always-on sensing devices require it since frequent recharging is the death knell of any electronic product. That’s why semiconductor companies are offering new ways to conserve power.“MEMS sensor suppliers have made significant strides in the power, size and performance of their devices,” said Aspinity CEO Tom Doyle. “Yet these gains deliver only incremental power improvements to the system.”Doyle advocates a new architectural model that uses an analog neuromorphic processor to analyze all sensor data at the start of the signal chain instead of sending it downstream so power-hungry chips such as DSPs can digitize it before analysis.“The technology industry wants to take advantage of the many benefits of always-on sensing applications,” said Doyle. “Before we can reach mass proliferation, however, we need to resolve the power issues that are deal-breakers for some applications. We believe the answer to this challenge is architectural. All the data gathered by always-on sensing systems is analog in nature, yet as soon as it’s captured, it’s digitized immediately for analysis. Determining which data is important up front eliminates the digitization and processing of irrelevant data so that voice-first devices such as smart speakers and wearables/hearables can run for long periods of time without requiring battery recharge.”Syntiant CTO Jeremy Holleman agreed that on-device intelligence is the future.“Did you just fall? Is your heartrate a bit off? Deep learning provides a toolset that yields vastly superior decisions,” said Holleman. “The problem is that deep learning is computationally intensive. The answer is a neural network that performs on-device edge inferencing.”Holleman added that Syntiant’s neural decision processor was recently certified as Amazon Voice Service (AVS)-compliant for wake-word detection, making it easier to design voice control in battery-powered devices such as earbuds and wearables.MSEC Technology Showcase WinnerWith the groundswell of interest in intelligence at the edge, it was no surprise that Cartesiam won top honors among all competitors in the MSEC Technology Showcase for its NanoEdge AI, software that brings AI to the edge of the signal chain, making it easier for designers to create intelligent objects that can learn and understand.“Unlike other AI algorithmic technologies for sensing devices, NanoEdge enables both learning and inference at the edge, providing accurate and adaptive intelligence,” said Cartesiam Managing Director and Co-founder Marc Dupaquier, who accepted the award. “It’s also the only tool of its kind that does not require data scientists on board for implementation, which saves a tremendous amount of money. Our clients can build a machine learning library and embed it into their own code within weeks to realize the same caliber of unsupervised neural network that was once the exclusive domain of AI cloud vendors.”MSIG 2019 Hall of FameAt this year’s conference, MSIG Director Carmelo Sansone recognized two longtime contributors to the commercialization of MEMS and sensors: Peter G. Hartwell, Ph.D., chief technology officer at InvenSense, a TDK group company; and Thomas Kenny, professor and senior associate dean of engineering at Stanford University.Hartwell leads technology strategy and the InvenSense advanced technology research group. He has more than 25 years’ experience commercializing silicon MEMS products, including advanced sensors and actuators, and developing MEMS testing techniques.Kenny’s academic accomplishments include authoring or co-authoring more than 250 scientific papers and holding 50 issued patents. He has also advised more than 50 graduated Ph.D. students from Stanford.MSEC 2020Mark your calendar for next year’s MSEC, October 12-14, at Coronado Island Marriott Resort Spa in Coronado, Calif. Get updates from MSIG on MSEC and other upcoming events including MSTC 2020.Stay in Touch with MSIGMEMS Sensors Industry Group (MSIG), a SEMI Strategic Association Partner, is the industry association representing the global MEMS and sensors supply chain. To learn how MSIG enables professionals in the MEMS and sensors industry to innovate, address common challenges and accelerate business results, visit us today.Connect with MSIG on Twitter and LinkedIn. Subscribe to SEMI Blog: Technology and Trends.Maria Vetrano is a public relations consultant at SEMI.

Part of 1 of 2-part series on MSEC 2019 highlights. Read Part 2. MEMS and sensors are proliferating across consumer, automotive, biomedical/healthcare, robotics, industrial and agriculture applications to harvest sensory data in a hyper-connected world and meet demand from consumers and organizations alike as they clamor for more intelligence in electronics.Take the ubiquitous iPhone. Shipped in 2007, Apple’s first iPhone sported five sensors. By contrast, the most feature-packed smartphones will embed up to 20 sensors by 2021, according to Yole Développement’s Jérôme Azémar. He estimates that the devices will feature four MEMS microphones, four CMOS image sensors (CIS), a RGB color sensor, a laser rangefinder, an infrared sensor, a gas sensor, a heart rate monitor and a fingerprint sensor, not to mention the MEMS inertial sensors that device users have come to know and trust.The MEMS market is expected to reach $18.5 billion in 2024 [1], up a whopping 60 percent from $11.6 billion in 2018, according to Azémar, who presented at MEMS Sensors Industry Group’s 15th annual MEMS Sensors Executive Congress (MSEC) in late October in Coronado, Calif. Add other types of sensors to the mix – CIS, environmental sensors, LiDARs, radars, ultrasonics, and fingerprint sensors – and the market will mushroom to $93 billion by 2024, said Azémar.Since MEMS Sensors Industry Group (MSIG) joined SEMI as a Strategic Association Partner three years ago, SEMI has expanded its MEMS and sensors programs to Europe and Asia while continuing to grow its U.S. conferences. “SEMI is continually investing in MEMS and sensors innovation across the supply chain,” said Dave Anderson, president of SEMI Americas and host of MSEC. “For example, MSIG is contributing to the development of the Heterogeneous Integration Roadmap, an initiative designed to drive heterogeneous integration technology development and accelerate electronics innovation. The roadmap spans device design, test and fabrication, ecosystem development, R D, equipment and materials. “At MSEC, executives and other speakers explored how AI and blockchain are remaking the food supply chain, air transportation and other sectors as MEMS and sensors improve the quality of our lives,” said Anderson.Sensing at the EdgeThe concept of artificial intelligence (AI), that a machine can harness intelligence that rivals or outperforms humans – and act without human intervention – has been a feature of the human imagination since at least the 1968 film 2001: A Space Odyssey. MEMS and sensors facilitate intelligence in a wide range of electronics such as smartphones, healthcare wearables, robots, industrial predictive maintenance systems, and cars. AI is sure to augment that functionality.MEMS and sensors are now in their third wave of evolution, a focus on edge AI, Bosch Sensortec CEO and General Manager Stefan Finkbeiner told MSEC attendees. For its part, Bosch is working to add AI to MEMS devices. The first wave integrated software with MEMS sensors, and the second, sensor fusion, enabled designers to allocate performance and power strategically to tune MEMS for resource-constrained devices. The third wave is “an active-learning phase in which MEMS facilitates real-time learning at the edge to promote greater personalization, environmental feedback, privacy of user data and improved battery life,” said Finkbeiner.Small sensor nodes with edge AI exemplify third-wave applications. Integrating low-power environmental sensors (e.g., gas, temperature, pressure, humidity and air-flow sensors), the nodes could be deployed in fire-prone forests to assess fire risk and support early detection. Access to this real-time environmental information could prove invaluable to residents and public-safety personnel alike.Google takes another tack, applying machine learning to resource-constrained devices, said Nick Kreeger, a senior software engineer at the Internet giant. The company’s Google Brain creates machine learning models that can run on inexpensive, low-power microcontrollers using Google’s TensorFlow Lite, an open-source machine learning tool that’s been deployed on a multitude of mobile devices. Inferencing is done at the device’s edge, rather than transmitted to the cloud.Meeting the power constraints of battery-powered sensing devices is another matter that starts with minimizing energy and data waste. “Deep learning is compute-bound and runs well on existing microcontrollers,” Kreeger said. “Because it’s all arithmetic, it’s low-power compared to storage access.”Already Google has worked with Plant Village, a research unit at Penn State University, and the International Institute of Tropical Agriculture (IITA) to help farmers improve food production by using machine learning and cheap sensors to spot and manage planet diseases in developing countries. And that production chain is in dire need of a boost, according to Rajendra Rao, general manager of IBM Food Trust, an enterprise-class blockchain solution.“We are on the cusp of complete failure of the food system,” Rao said. “One out of 10 people gets sick each year from foodborne illness, 420,000 die from this annually, 80 percent of companies in the food supply chain have not digitized, one-third of all fresh food in the US is thrown away, and one in five seafood samples worldwide is mislabeled.”IBM Food Trust’s work with Sucafina, which manages a global green coffee supply chain, shows how sensors can trace food from the farm to the processing plant to the consumer. With the IBM Food Trust platform, Sucafina can track the origin of the beans used in a cup of coffee – a competitive differentiator to coffee drinkers eager to support fair-trade coffee roasters.ripe.io, one of Forbes’ 25 most innovative AgTech startups, is also tackling the challenges and complexities of the food supply chain.“Our secure blockchain platform creates a digital twin of food items, transparently aggregating foods’ journey in real-time, to provide a harmonized trustworthy platform for multiple stakeholders,” said Rachel Gabato, the company’s COO. The ripe.io blockchain-based platform collects data from various sensors – temperature, pressure, light, humidity and inertial MEMS sensors. Growers, distributors and end customers including sweetgreen – a U.S. restaurant chain that depends on fresh produce – use the information to trace the origin and quality of food.MSEC 2020Mark your calendar for next year’s MSEC, October 12-14, at Coronado Island Marriott Resort Spa in Coronado, Calif. Get updates from MSIG on MSEC and other upcoming events including MSTC 2020.Stay in Touch with MSIGMEMS Sensors Industry Group (MSIG), a SEMI Strategic Association Partner, is the industry association representing the global MEMS and sensors supply chain. To learn how MSIG enables professionals in the MEMS and sensors industry to innovate, address common challenges and accelerate business results, visit us today.Connect with MSIG on Twitter and LinkedIn. Subscribe to SEMI Blog: Technology and Trends.[1] Source: Status of the MEMS Industry report, Yole Développement, 2019Maria Vetrano is a public relations consultant at SEMI.

Semiconductor, electronics and equipment manufacturers today face a number of logistics and supply chain challenges that could be overcome by systems providing a secure, tamper-resistant, single source of truth. Chief among these challenges is limited data sharing due to data security barriers among suppliers, shippers, manufacturers and test houses, an impediment to achieving optimal product quality and regulatory compliance. Additionally, inefficient and inadequate processes for tracking goods make it more difficult to isolate shipping problems, track faulty parts and verify product authenticity. Counterfeiting has become a serious problem that costs US-based semiconductor manufacturers $7.5 billion annually.How Blockchain Can Help Clear Data Sharing BottlenecksBlockchain functions could help alleviate many data sharing pain points in manufacturing. Blockchain’s distributed functionality, bundled security measures, and associated features such as smart contracts have the potential to help manufacturers quickly trace goods, manage records transparently, and automate supply chain processes and payments. No isolated blockchain platform would solve all of these problems on its own. But, when combined with other solutions and applied to particular use cases, blockchain has the potential to optimize operations and foster an environment of trust and collaboration among consortium members. Three core features of blockchain make it a valuable technology for manufacturing: Distributed and immutable system of record. With a distributed system of record in the blockchain network, there is no "central" data store controlled by one organization. The distributed ledger provides all participants with a view into the data, thus increasing transparency, data distribution timeliness, information sharing, and data access. Security also improves as there is no single central data store open to external attacks. Once data is inserted onto the chain, it cannot be easily changed. Security and Trust. Blockchain integrates best-of-breed cryptographic mechanisms to guarantee the digital identity of the network participants and secure the privacy of the data stored to enable role-based data access. It brings trust to a potentially trustless environment without the need for a centralized third party. Smart Contracts. Smart contracts are embedded business logic that can be added to a blockchain. They enable the automation of many processes and the secure handling of contracts. Blockchain Use Cases in ManufacturingIn each stage of manufacturing, blockchain could be applied in a variety of use cases to expedite processes and alleviate security issues. A few examples that merely scratch the surface of what may be possible follow.In pre-production, manufacturers may implement blockchain solutions for Collaborative Planning, Forecasting and Replenishment (CPFR). These systems monitor inventory levels, enabling suppliers to replenish supplies before they run low. The expensive, proprietary B2B networks used today could be replaced with blockchain as the common sharing protocol, using non-proprietary or public networks.Suppliers may also combine blockchain with IoT sensors on shipping containers to provide a tamper-resistant record of shipping conditions. This could be used to ensure that temperature and humidity tolerances for chemicals and equipment are not exceeded during transit from the supplier. The identity and materials in components and subcomponents of manufacturing equipment could be collected on a blockchain to verify compliance with environmental and health regulations. During production, a manufacturing process machine can be registered on a blockchain with a unique identity; its performance and maintenance history can be recorded. A maintenance service provider could then be automatically notified, via a smart contract, when a predictive maintenance alert is written, allowing repair of machines before they fail. In the distribution stage, customers could search the ledger for a product’s complete history, reducing counterfeiting and solidifying the origin of properly sourced goods. When faulty product is identified, the manufacturer may search the ledger to quickly locate the faulty supplier or bad test results and alert all receivers of the defective product.ConclusionWith blockchain, manufacturing can become a more collaborative process among suppliers, manufacturers and customers. Blockchain can help streamline the supply chain and inventory replenishment, improve tracking and regulatory compliance, and reduce counterfeiting. Augmenting blockchain with IoT enables use cases like predictive maintenance and monitoring of goods during transit. Blockchain is not yet mature and its business value still needs to be proven. However, it is poised to help manufacturers decrease costs and fraud, and provide customers with faster, more secure delivery, increased visibility, and consistency.More Resources on Blockchain and ManufacturingTibco is an active member of SEMI’s Smart Manufacturing Technology Community, which holds regular meetings on this and other topics. Join now to help shape the future of Smart Manufacturing. For more information on blockchain use cases in manufacturing, please see these resources. Read this Whitepaper: Blockchain and Manufacturing: A Match Made in the Factory Watch this Webinar: Blockchain and Manufacturing - A Match Made in the Factory Visit the TIBCO Blockchain Solutions page Mike Alperin is a TIBCO principal manufacturing industry consultant embedded in the Data Science team where he applies analytics, machine learning and big data technology to current industry problems. Prior to this he was the product manager for a leading commercial yield management application. He has worked at start-ups and global semiconductor manufacturing companies as a yield manager, device engineer, process engineer and failure analyst. Mike is based in Austin, Texas.