Category Archives: Manufacturing

By Nishita Rao

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.

By Nishita Rao

ULVAC Technologies’ David Mount is working with The CIA. Is he the Jack Reacher of the MEMS and sensors industry, jetting around the world to secret meetings, you wonder? While David isn’t quite the super-spy that you might have imagined, he is doing some fascinating work on behalf of ULVAC Technologies, the world leader in vacuum technology.

ULVAC has been collaborating with The Culinary Institute of America (CIA) on Menus of Change, “a ground-breaking initiative from The Culinary Institute of America and Harvard T.H. Chan School of Public Health that works to realize a long-term, practical vision integrating optimal nutrition and public health, environmental stewardship and restoration, and social responsibility concerns within the foodservice industry and the culinary profession.”

ULVAC also partners with Menus of Change (MOC) University Research Collaborative, a group of elite universities and food-service executives working together to “accelerate efforts to move Americans toward healthier, more sustainable, plant-forward diets.”

MEMS & Sensors Industry Group’s Nishita Rao caught up with David, a featured speaker at MEMS & Sensors Executive Congress on October 29-30, 2018, in Napa, Calif. to give MSEC attendees a preview of David’s talk.

SEMI: How did ULVAC get involved with The CIA on Menus of Change?

Mount: People in the MEMS & sensors industry may not know that ULVAC started as an equipment supplier to the food industry. In 1952 ULVAC began supplying freeze-drying equipment – which relies on vacuum technology — to food companies tasked with providing long-lasting foods and beverages for the U.S. military under the Marshall Plan. Think instant soup, ramen noodles and Tang. While ULVAC’s technology portfolio is now very broad — spanning deposition equipment for the semiconductor industry, vacuum brazing for automotive, and even vacuum freeze-drying of vaccines that can be shipped dry but combined with distilled water for administration — the company has kept a hand in food technology. ULVAC’s vacuum cooling equipment rapidly and safely cools foods, dramatically increasing shelf life.

The CIA is at the forefront of innovation in food technology, so we worked with them to test a vacuum cooling system that can also be used in the kitchen or in the field. In the Central Valley of California, for example, it can be 104ºF in the fields where lettuce is picked; our vacuum cooling system can cool that lettuce down to 47ºF in minutes.

The CIA is also developing prepared foods for industrial settings such as university cafeterias and airlines. A prepared chicken dish, for example, might be cooked at 350ºF and then cooled to refrigeration temperatures. The potential problem is that bacteria can grow when you cool that food for storage. Some of The CIA test kitchens in California are using ULVAC’s vacuum cooling system to quickly and safely cool prepared foods.

Vacuum-cooling is just one stage in food production, of course. Sensors are also widely used in food production and safety.

SEMI: How do The CIA test kitchens use sensors?

Mount: Nearly all aspects of production, processing and management in agricultural and food systems involve measurement of product and resource attributes. Sensors are a natural fit here as they can provide inspection capabilities that are accurate, fast and consistent. I plan to dive into some specific examples of the ways that The CIA and the MOC Research Collaborative are employing sensors to increase the safety of food and agricultural production.

SEMI: What would you like MSEC attendees to take away from your presentation?

Mount: I love knowing that the work that we do in this industry can benefit humanity. Applying our various technologies to food and agricultural production is just one way to do that. I encourage MSEC attendees to explore those markets that improve human quality of life – as well as the life and health of our planet and its other inhabitants.

ULVAC Technologies senior advisor David Mount is a 35-year veteran of the vacuum and thin film equipment industry. He tried to retire from ULVAC but they would not let him go! David consults with ULVAC on strategic projects such as the company’s collaboration with the CIA.

He will present Sensors in Food and Agriculture on Tuesday, October 30 at the MEMS & Sensors Executive Congress.

Register today to learn more about how sensors are transforming the food industry.

Nishita Rao is a marketing manager at SEMI.

Originally published on the SEMI blog.

Cynthia Wright, a retired military officer with over 25 years of experience in national security and cyber strategy and policy, now Principal Cyber Security Engineer at The MITRE Corporation, will give the opening keynote at the upcoming MEMS & Sensors Executive Congress, October 29-30, 2018 in Napa, Calif. SEMI’s Maria Vetrano interviewed Wright to give MSEC attendees an advance look at Wright’s highly anticipated presentation.

SEMI: MEMS and sensors suppliers provide intelligent sensing and actuation to hundreds of billions of autonomous mobility devices – but historically, our community has not been at the forefront of cybersecurity. Why is now a good time for us to get involved?

Wright: From wearables, smartphones, refrigerators and agriculture to medical devices and military hardware, autonomous mobility devices pervade our lives. At the same time, Internet of Things (IoT) botnet attacks like Mirai — and other demonstrated cyberattacks on home devices, vehicles and infrastructure — highlight the increasingly urgent need to address cybersecurity and privacy in MEMS/sensors-enabled devices.

As building-block players in autonomous devices, MEMS and sensors suppliers have several good reasons to get involved.

The number of IoT cyber security bills before state and federal legislatures suggest that regulation is coming, and it is in everyone’s best interest to prepare. While original equipment manufacturers (OEMs) would generally be held liable in cases of component malfunction or data breach, if insecurity stems from a microelectromechanical component, OEMs would most likely choose component suppliers with secure products.

Beyond legislation and competitive advantage, we must consider that people’s well-being, even lives, could be at stake. Imagine what could happen if someone hacks into an insulin pump, the accelerometer on a train, or the LIDAR of an autonomous car. Intrusions of this sort could prove catastrophic.

SEMI: Where do you perceive the biggest potential threats to consumers, industry, government?

Wright: In good military fashion, I would say that it depends. If a person is a consumer of medical implants, that’s a big threat. On the government side, we could be talking about networked devices involved in military situational awareness. In industry, it could be sensors governing critical manufacturing or safety processes.

I am not saying that every sensor must be secure. In every sector, there are areas of greater or lesser vulnerability, depending on context.

SEMI: What is security or privacy by design?

Wright: Addressing security flaws is cheaper and more easily accomplished at the design stage and not after the vulnerabilities are discovered. At MITRE, we practice systems- and design-oriented thinking as we consult with people doing development. We help them to develop security standards and approaches that are broadly applicable, rather than focusing on a specific product.

For example, MITRE looks at the ways that a person might hack into a car to steal location and life history data — or alter its functions — to facilitate general standards and approaches that will help manufacturers better ensure the privacy and security of autonomous vehicles. Hackers have demonstrated that they can interfere with vehicle transmissions and brakes. Ignition, steering and other critical systems are theoretically accessible through the same types of attacks. To what degree can MEMS/sensors suppliers help automotive manufacturers ensure the privacy and security of autonomous cars, and the safety of their drivers?

SEMI: What would you like MSEC attendees to take away from your presentation?

Wright: MEMS/sensors suppliers are on the leading edge of computing and should take some responsibility for considering cybersecurity and privacy, for the safety of their customers and their own competitive advantage. Recognize which devices should be secure and act accordingly. Get involved at the design stage. The market for secure microelectronics is only going to grow, and this will benefit suppliers who take secure design seriously.

Cynthia Wright will present Cyber Security and Privacy in the Age of Autonomous Sensing on Monday, October 29 at MEMS & Sensors Executive Congress in Napa, Calif.

Register today to connect with her at the event.

Maria Vetrano is a public relations consultant at SEMI.

A new approach in Fault Detection and Classification (FDC) allows engineers to uncover issues more thoroughly and accurately by taking advantage of full sensor traces.

By Tom Ho and Stewart Chalmers, BISTel, Santa Clara, CA

Traditional FDC systems collect data from production equipment, summarize it, and compare it to control limits that were previously set up by engineers. Software alarms are triggered when any of the summarized data fall outside of the control limits. While this method has been effective and widely deployed, it does create a few challenges for the engineers:

  • The use of summary data means that (1) subtle changes in the process may not be noticed and (2) the unmonitored section of the process will be overlooked by a typical FDC system. These subtle changes or the missed anomalies in unmonitored section may result in critical problems.
  • Modeling control limits for fault detection is a manual process, prone to human error and process drift. With hundreds of thousandssensors in a complex manufacturing process, the task of modeling control limits is extremely time consuming and requires a deep understanding of the particular manufacturing process on the part of the engineer. Non-optimized control limits result in misdetection: false alarms or missed alarms.
  • As equipment ages, processes change. Meticulously set control limit ranges must be adjusted, requiring engineers to constantly monitor equipment and sensor data to avoid false alarms or missed real alarm.

Full sensor trace detection

A new approach, Dynamic Fault Detection (DFD) was developed to address the shortcomings of traditional FDC systems and save both production time and engineer time. DFD takes advantage of the full trace from each and every sensor to detect any issues during a manufacturing process. By analyzing each trace in its entirety, and running them through intelligent software, the system is able to comprehensively identify potential issues and errors as they occur. As the Adaptive Intelligence behind Dynamic Fault Detection learns each unique production environment, it will be able to identify process anomalies in real time without the need for manual adjustment from engineers. Great savings can be realized by early detection, increased engineer productivity, and containment of malfunctions.

DFD’s strength is its ability to analyze full trace data. As shown in FIGURE 1, there are many subtle details on a trace, such as spikes, shifts, and ramp rate changes, which are typically ignored or go undetected by a traditional FDC systems, because they only examine a segment of the trace- summary data. By analyzing the full trace using DFD, these details can easily be identified to provide a more thorough analysis than ever before.

Figure 1

Dynamic referencing

Unlike traditional FDC deployments, DFD does not require control limit modeling. The novel solution adapts machine learning techniques to take advantage of neighboring traces as references, so control limits are dynamically defined in real time.  Not only does this substantially reduce set up and deployment time of a fault detection system, it also eliminates the need for an engineer to continuously maintain the model. Since the analysis is done in real time, the model evolves and adapts to any process shifts as new reference traces are added.

DFD has multiple reference configurations available for engineers to choose from to fine tune detection accuracy. For example, DFD can 1) use traces within a wafer lot as reference, 2) use traces from the last N wafers as reference, 3) use “golden” traces as reference, or 4) a combination of the above.

As more sensors are added to the Internet of Things network of a production plant, DFD can integrate their data into its decision-making process.

Optimized alarming

Thousands of process alarms inundate engineers each day, only a small percentage of which are valid. In today’s FDC systems, one of the main causes for false alarms is improperly configured Statistical Process Control (SPC) limits. Also, typical FDC may generate one alarm for each limit violation resulting in many alarms for each wafer process. DFD implementations require no control limits, greatly reducing the potential for false alarms.  In addition, DFD is designed to only issues one alarm per wafer, further streamlining the alarming system and providing better focus for the engineers.

Dynamic fault detection use cases

The following examples illustrate actual use cases to show the benefits of utilizing DFD for fault detection.

Use case #1– End Point Abnormal Etching

In this example, both the upper and lower control limits in SPC were not set at the optimum levels, preventing the traditional FDC system from detecting several abnormally etched wafers (FIGURE 2).  No SPC alarms were issued to notify the engineer.

Figure 2

On the other hand, DFD full trace comparison easily detects the abnormality by comparing to neighboring traces (FIGURE 3).  This was accomplished without having to set up any control limits.

Figure 3

Use case #2 – Resist Bake Plate Temperature

The SPC chart in Figure 4 clearly shows that the Resist bake plate temperature pattern changed significantly; however, since the temperature range during the process never exceeded the control limits, SPC did not issue any alarms.

Figure 4

When the same parameter was analyzed using DFD, the temperature profile abnormality was easily identified, and the software notified an engineer (FIGURE 5).

Figure 5

Use case #3 – Full Trace Coverage

Engineers select only a segment of sensor trace data to monitor because setting up SPC limits is so arduous. In this specific case, the SPC system was set up to monitor only the He_Flow parameter in recipe step 3 and step 4.  Since no unusual events occurred during those steps in the process, no SPC alarms were triggered.

However, in that same production run, a DFD alarm was issued for one of the wafers. Upon examination of the trace summary chart shown in FIGURE 6, it is clear that while the parameter behaved normally during recipe step 3 and step 4, there was a noticeable issue from one of the wafers during recipe step 1 and step 2.  The trace in red represents the offending trace versus the rest of the (normal) population in blue. DFD full trace analysis caught the abnormality.

Figure 6

Use case #4 – DFD Alarm Accuracy

When setting up SPC limits in a conventional FDC system, the method of calculation taken by an engineer can yield vastly different results. In this example, the engineer used multiple SPC approaches to monitor parameter Match_LoadCap in an etcher. When the control limits were set using Standard Deviation (FIGURE 7), a large number of false alarms were triggered.  On the other hand, zero alarms were triggered using the Meanapproach (FIGURE 8).

Figure 7

Figure 8

Using DFD full trace detection eliminates the discrepancy between calculation methods. In the above example, DFD was able to identify an issue with one of the wafers in recipe step 3 and trigger only one alarm.

Dynamic fault detection scope of use

DFD is designed to be used in production environments of many types, ranging from semiconductor manufacturing to automotive plants and everything in between. As long as the manufacturing equipment being monitored generates systematic and consistent trace patterns, such as gas flow, temperature, pressure, power etc., proper referencing can be established by the Adaptive Intelligence (AI) to identify abnormalities. Sensor traces from Process of Record (POR) runs may be used as starting references.

Conclusion

The DFD solution reduces risk in manufacturing by protecting against events that impact yield.  It also provides engineers with an innovative new tool that addresses several limitations of today’s traditional FDC systems.  As shown in TABLE 1, the solution greatly reduces the time required for deployment and maintenance, while providing a more thorough and accurate detection of issues.

 

TABLE 1
FDC

(Per Recipe/Tool Type)

 DFD

(Per Recipe/Tool Type)
FDC model creation 1 – 2 weeks < 1 day
FDC model validation and fine tuning 2 – 3 weeks < 1 week
Model Maintenance Ongoing Minimal
Typical Alarm Rate 100-500/chamber-day < 50/chamber-day
% Coverage of Number of Sensors 50-60% 100% as default
Trace Segment Coverage 20-40% 100%
Adaptive to Systematic Behavior Changes No Yes

 

 

TOM HO is President of BISTel America where he leads global product engineer and development efforts for BISTel.  [email protected].   STEWART CHALMERS is President & CEO of Hill + Kincaid, a technical marketing firm. [email protected]. 

The Micron Foundation (Nasdaq:MU) announced a $1 million grant for universities and nonprofit organizations to conduct research into how artificial intelligence (AI) can improve lives while ensuring safety, security and privacy. The grant was announced at the inaugural Micron Insight 2018 conference where the technology industry’s top minds gathered in San Francisco to discuss the future of AI, machine learning and data science, and how memory technology is essential in bringing intelligence to life.

“Artificial intelligence is one of the frontiers where science and engineering education can best be applied,” said Micron Foundation Executive Director Dee Mooney. “We want to accelerate advances in AI by investing in education and making sure that pioneers of this technology, reflect the diversity and richness of the world we live in and build a future where AI benefits everyone.”

Micron awarded a total of $500,000 to three initial recipients at Micron Insight 2018.

  • AI4All, a nonprofit organization, works to increase diversity and inclusion in AI education, research, development and policy. AI4All supports the next generation of diverse AI talent through its AI Summer Camp. Open to 9th-11th grade students, the camp gives special consideration to young women, underrepresented groups and families of lower socioeconomic status.
  • Berkeley Artificial Intelligence Research (BAIR) Lab supports researchers and graduate students developing fundamental advances in computer vision, machine learning, natural-language processing, planning and robotics. BAIR is based at UC Berkeley’s College of Engineering.
  • In a related announcement, the Micron Foundation launched a $1 million grant for universities and non-profit organizations to conduct research on AI. For more details, visit http://bit.ly/MicronFoundation.

The $1 million fund is available to select research universities focused on the future implications of AI in life, healthcare and business, with a portion specifically allocated to support women and underrepresented groups. The Micron Foundation supports researchers tackling some of AI’s greatest challenges – from building highly reliable software and hardware programs to finding solutions that address the business and consumer impacts of AI.

In August 2018, the Micron Foundation announced a $1 million fund for Virginia colleges and universities to advance STEM and STEM-related diversity programs in connection with Micron’s expansion of its memory production facilities in Manassas, Virginia.

Technion, Israel’s technological institute, announced this week that Intel is collaborating with the institute on its new artificial intelligence (AI) research center. The announcement was made at the center’s inauguration attended by Dr. Michael Mayberry, Intel’s chief technology officer, and Dr. Naveen Rao, Intel corporate vice president and general manager of the Artificial Intelligence Products Group.

“AI is not a one-size-fits-all approach, and Intel has been working closely with a range of industry leaders to deploy AI capabilities and create new experiences. Our collaboration with Technion not only reinforces Intel Israel’s AI operations, but we are also seeing advancements to the field of AI from the joint research that is under way and in the pipeline,” said Naveen Rao, Intel corporate vice president and general manager of Artificial Intelligence Products Group

The center features Technion’s computer science, electrical engineering, industrial engineering and management departments, among others, all collaborating to drive a closer relationship between academia and industry in the race to AI. Intel, which invested undisclosed funds in the center, will represent the industry in leading AI-dedicated computing research.

Intel is committed to accelerating the promise of AI across many industries and driving the next wave of computing. Research exploring novel architectural and algorithmic approaches is a critical component of Intel’s overall AI program. The company is working with customers across verticals – including healthcare, autonomous driving, sports/entertainment, government, enterprise, retail and more – to implement AI solutions and demonstrate real value. Along with Technion, Intel is also involved in AI research with other universities and organizations worldwide.

Intel and Technion have enjoyed a strong relationship through the years, as generations of Technion graduates have joined Intel’s development center in Haifa, Israel, as engineers. Intel has also previously collaborated with Technion on AI as part of the Intel Collaborative Research Institute for Computational Intelligence program.

MEMS & Sensors Industry Group (MSIG), a SEMI Strategic Association Partner, today announced four Technology Showcase finalists for the 14th annual MEMS & Sensors Executive Congress (MSEC), October 28-30, 2018, at the Silverado Resort and Spa in Napa, Calif. The MEMS & Sensors Executive Congress is the premier event for industry executives to gain insights on emerging MEMS and sensors opportunities and network with partners, customers and competitors. An early bird registration discount is available until Oct. 8.

The Technology Showcase highlights the latest applications enabled by MEMS and sensors as finalists demonstrate their innovations and vie for attendee votes. The finalists were selected by a committee of industry experts.

Technology Showcase Finalists

N5 Sensors’ Micro-Scale Gas Sensors on a Chip enable low-power, high-reliability microscale gas and chemical sensing technologies in small-footprint devices. The chip promises to broaden the implementation of gas and chemical sensing for industrial detection, first response, smart cities, demand-controlled ventilation, wearables and other consumer electronics. N5 Sensors Logo
NXP Semiconductor’s Asset Tracking Technology uses motion sensors, GPS and edge computing for precision tracking of a package’s journey from origin to delivery point. The technology enables logistics companies to quickly pinpoint and resolve transportation issues. See video NXP Logo
Scorched Ice Inc.’s Smart Skates leverage STMicroelectronics’ inertial measurement unit (IMU) sensors to facilitate real-time diagnostics of a hockey player’s skating technique, condition and performance. The device provides actionable insights to players, coaches, trainers and scouts. SI Logo
SportFitz’s Concussion-Monitoring Device combines real-time measurements of location, position, direction and force of impact as well as big data analytics and embedded protocols to stream data that can help assess potentially concussive brain impacts. The one-inch wearable device is hypoallergenic, waterproof, recyclable, reusable and rechargeable. See video. SportsFitz Logo

 

The world is edging closer to a reality where smart devices are able to use their owners as an energy resource, say experts from the University of Surrey.

In a study published by the Advanced Energy Materials journal, scientists from Surrey’s Advanced Technology Institute (ATI) detail an innovative solution for powering the next generation of electronic devices by using Triboelectric Nanogenerators (TENGs). Along with human movements, TENGs can capture energy from common energy sources such as wind, wave, and machine vibration.

A TENG is an energy harvesting device that uses the contact between two or more (hybrid, organic or inorganic) materials to produce an electric current.

Researchers from the ATI have provided a step-by-step guide on how to construct the most efficient energy harvesters. The study introduces a “TENG power transfer equation” and “TENG impedance plots”, tools which can help improve the design for power output of TENGs.

Professor Ravi Silva, Director of the ATI, said: “A world where energy is free and renewable is a cause that we are extremely passionate about here at the ATI (and the University of Surrey) – TENGs could play a major role in making this dream a reality. TENGs are ideal for powering wearables, internet of things devices and self-powered electronic applications. This research puts the ATI in a world leading position for designing optimized energy harvesters.”

Ishara Dharmasena, PhD student and lead scientist on the project, said: “I am extremely excited with this new study which redefines the way we understand energy harvesting. The new tools developed here will help researchers all over the world to exploit the true potential of triboelectric nanogenerators, and to design optimised energy harvesting units for custom applications.”

Leti, a research institute of CEA Tech, and EFI Automotive, an international supplier of sensors, actuators and embedded smart modules for the automotive industry, today announced a project to dramatically improve reliability and response time of low-cost automotive components by equipping the devices with sophisticated model predictive control techniques.

Model predictive control (MPC) is an advanced method of process control that makes use of a model of the system to predict its behavior. The control law is based on an optimization technique that computes the system inputs, taking into account the reference that the system output has to follow, together with the effort (energy) that is applied on the system inputs and some constraints that may exist within the system, typically saturation of the system inputs.

MPC also allows electronics equipment to perform at levels that are not possible with standard control laws, e.g. proportional-integral-derivative (PID) controllers. But this sophisticated technique is rarely used on low-cost, low-capability computing units, because it requires solving optimization problems under constraints, which is a complex computational task.

Leti and EFI Automotive are evaluating the implementation of MPC on low-cost, low-computational-capability computing platforms, such as microcontrollers or low-cost digital signal processors (DSPs). The goal is to improve the dynamics of the systems considered, because automotive certification is easier when the control law is implemented on a DSP or a microcontroller. An example of EFI Automotive product, which will benefit from the MPC implementation, is the Air Loop Actuator (Figure 1).

Figure 1: EFI Air Loop Actuator Prototype (200ms response time). Numerical command and power stage integrated

“The control community, including academic researchers and process control experts in industry, is trying to make MPC available for these systems by resolving the underlying optimization problem on a low computational-capability computing platform,” said Marie-Sophie Masselot, business development manager, Leti. “This shortcoming usually leads to suboptimal performance for the controlled system. Our project with EFI Automotive will take into account specifics to offset the drop in performance, or response time, introduced when solving the model predictive control problem on this low computational-capability computing platform.”

In addition to transferring its expertise in MPC to EFI Automotive, Leti will develop software-automation tools dedicated to a given problem as a feasibility demonstration for the MPC project, and then make the tools easily expandable to similar control challenges.

For example, Leti and EFI will develop an MPC law for a given system and, with its increased expertise, EFI will expand this control technique to other systems.

“By combining Leti’s MPC expertise with our know-how in real-time processing on low-cost, low-computational capability computing units, we expect to dramatically improve the response time and reliability of our devices that are key to operating today’s complex vehicles,” said Vincent Liebart, innovation engineer at EFI Automotive.

 

In its Mid-Year Update to the 2018 McClean Report, IC Insights updated its forecast of sales growth for each of the 33 major IC product categories defined by WSTS (Figure 1).  IC Insights now projects that seven product categories will exceed the 16% growth rate expected from the total IC market this year. For the second consecutive year, the DRAM market is forecast to top all IC product segments with 39% growth. Overall, 13 product categories are forecast to experience double-digit growth and 28 total IC product categories are expected to post positive growth this year, down slightly from 29 segments in 2017.

Rising average selling prices for DRAM continued to boost the DRAM market through the first half of the year and into August.  However, IC Insights believes the DRAM ASP (and subsequent market growth) is at or near its peak, as a big rise in DRAM capital expenditures for planned capacity upgrades and expansions is likely put the brakes on steep market growth beginning in 2019.

In second place with 29% growth is the Automotive—Special-Purpose Logic market, which is being lifted by the growing number of onboard electronic systems now found on new cars. Backup cameras, blind-spot (lane-departure) detectors, and other “intelligent” systems are mandated or are being added across all new vehicles—entry level to luxury—and are expected to contribute to the semiconductor content per new car growing to more than $540 per vehicle in 2018.

Wireless Comm—Application-Specific Analog is forecast to grow 23% in 2018, as the world becomes increasingly dependent on the Internet and demand for wireless connectivity continues to rise. Similarly, demand for medical/health electronics systems connectivity using the Internet will help the market for Industrial/Other Application-Specific Analog outpace total IC market growth in 2018.

Among the seven categories showing better than total IC market growth this year, three are forecast to be among the largest of all IC product categories in terms of dollar volume. DRAM (#1 with $101.6 billion in sales), NAND Flash (#2 with $62.6 billion), Computer and Peripherals—Special Purpose Logic (#4 with $27.6 billion) prove that big markets can still achieve exceptional percentage growth.

Figure 1