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sensors “New” MEMS evolution coming, predicts Yole By Peter Clarke A change is ocuring in the technology options behind MEMS sensors, according to Yole Developpement. The market for MEMS devices based on 3D packaging, novel materials and detection principles will grow from $1.6 billion in 2013 to $1.8 billion in 2014 and then accelerate to about $3.7 billion in 2019, according to market analysis firm Yole Developpement. This places “new” MEMS as being worth about 14 percent of the total MEMS market in 2013 of $11.7 billion rising to about 15 percent by 2019, when Yole reckons the total MEMS market will be $24 billion. “For conventional MEMS it is becoming harder to both decrease cost and size,” said Eric Mounier author of a report on new detection principles and the technical evolution of MEMS and NEMS for Yole. “People need to find different ways and these include maximum optimization for a given process Detection principles; a time line for possible product introduction. Source: Yole and principle of detection and the use of 3D stacking and packaging. Bosch, ST and mCube already use TSVs. This can push forward the miniaturization of MEMS.” But there is also increasing use of novel principles of detection such as piezoresistance, he said. The use of lead zirconate titanate (PZT) as a variable resistance is not at all new in MEMS and was used in some of the earliest silicon diaphragm pressure sensors. However, it is true that capacitive sensing is used almost exclusively for inertial MEMS sensing. Now there are companies such as Tronics using PZT and Qualtre using bulk acoustic wave (BAW) materials to implement gyroscopes. The same is being used for wafer-level autofocus and activation of micro-mirrors, Mounier said. “IBM is developing RF switches based on PZT,” he added. Although the PZT material is better established and is being deployed at such companies as Rohm Semiconductor, STMicroelectronics, Silex and Tronics, aluminum nitride may be interesting in the long-term as a more stable and fab-friendly material, said Mounier. “I think big foundries such as TSMC and Globalfoundries are interested in developing some of these new processes; it depends on volumes. But PZT for autofocus is a very big market, millions of units per year,” said Mounier. Mounier added that taking the example of Tronics in Grenoble he could imagine a smaller manufacturer wanting to get into consumer electronics but lacking the capacity to provide consumer volumes for whom a deal with a TSMC or a Globalfoundries to share From micromachines to MEMS to NEMS. production would make sense. Can Big-data solution free EVs from range anxiety? By Paul Buckley Researchers from North Carolina State University have developed software that estimates how much farther electric vehicles can drive before they will need to recharge. The software technique requires drivers to plug in their destination and automatically pulls in data on a host of variables to predict energy use for the vehicle. “Electric cars already have range-estimation software, but we believe our approach is more accurate,” explained Dr. Habiballah Rahimi- Eichi, a postdoctoral researcher at NC State and lead author of a paper on the work. “Existing technologies estimate remaining range based on average energy consumption of the past five miles, 15 miles, etc.,” said Rahimi-Eichi. “By plugging in the destination, our software looks at traffic data, whether you’ll be on the highway or in the city, weather, road grade, and other variables. This predictive, bigdata approach is a significant step forward, reducing the range estimation error to a couple of miles. In some case studies, we were able to get 95 percent range estimation accuracy.” The software takes all of the data related to the route between starting point and destination and uses big data techniques to determine which pieces of information are important and extract key features that can be plugged into an algorithm to estimate how far the vehicle can go before recharging. But two other variables are also plugged into the algorithm: the performance characteristics of the vehicle and its battery; and the amount of charge remaining in the battery. The state of charge is estimated using a patented technique developed by Rahimi-Eichi and Dr. Mo-Yuen Chow in 2012. Chow is a professor of electrical and computer engineering at NC State and a co-author of the paper. “People have a lot of ‘range anxiety’ in regard to electric vehicles – they’re afraid they’ll get stuck on the side of the road,” said Chow . “Hopefully, our new range estimation software will make people more confident about using electric vehicles.” 6 Electronic Engineering Times Europe November 2014 www.electronics-eetimes.com


EETE NOV 2014
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