Four key areas of multi-axis sensor technology to watch in the future

2020-11-06

With breakthroughs in cutting-edge technologies in materials science, nanotechnology, microelectronics, and other fields, and driven by the needs of economic and social development, four major areas may become the focus of future development in sensor technology.

First is wearable applications. According to ABI Research, the number of wearable sensors will reach 160 million in 2017. Wearable devices, represented by Google Glass, are the most noteworthy hardware innovations. Google Glass incorporates more than 10 types of sensors, including gyroscopes, accelerometers, magnetometers, and linear acceleration sensors, enabling functions that traditional terminals cannot achieve, such as taking a picture with a blink of an eye. Currently, the applications of wearable devices are expanding from external watches, glasses, and shoes to broader areas, such as electronic skin. Recently, the University of Tokyo has developed a flexible wearable sensor that can be attached to the skin. This sensor is film-like, weighing only 3g/m2 per unit area, about 1/27 of ordinary paper, and only 2 micrometers thick.

Second is autonomous driving. IHS Markit points out that sensor technology applications to advance autonomous driving are rapidly breaking through. In this field, Google's self-driving car project has achieved significant results. By installing cameras, radar sensors, and lidar inside the car, it generates real-time road condition information around the vehicle at intervals of 20 times per second. This information is analyzed using artificial intelligence software to predict future road conditions and combined with Google Maps for navigation. Google's self-driving cars have been granted permission to operate on roads in Nevada, Florida, and California. Global automotive giants such as Audi, Mercedes-Benz, BMW, and Ford have all launched autonomous driving technology research and development, with some models nearing mass production.

Third is medical care and health monitoring. Many medical research institutions at home and abroad, including internationally renowned medical giants, have made significant progress in applying sensor technology to the medical field. For example, ROHM is currently developing an image sensor using near-infrared light (NIR). Its principle is to irradiate near-infrared LEDs and then capture the reflected light using a special imaging element. By changing the wavelength of the near-infrared light, images are obtained, and image processing makes blood vessels clearer. Some research institutions have made progress in manufacturing sensors that can be embedded or ingested in the body. For example, the Georgia Institute of Technology is developing an in-body embedded sensor with pressure sensors and wireless communication circuits. This device is made of conductive metal and insulating films and can detect pressure changes based on changes in the frequency of the resonant circuit. After fulfilling its function, it dissolves in body fluids.

Fourth is industrial control. In 2012, GE's report "The Industrial Internet: Breaking the Boundaries of Intelligence and Machines" proposed that connecting humans and machines through intelligent sensors, combined with software and big data analysis, can break through the limitations of physics and materials science and change the way the world operates. The report also pointed out that by deploying the Industrial Internet, the United States could achieve a 1% efficiency improvement across industries, and the energy industry would save 1% of fuel (approximately $66 billion) within 15 years. In January 2013, GE installed more than 10,000 sensors in a battery manufacturing plant in New York to monitor data such as temperature, energy consumption, and air pressure during production. Factory managers can access this data through iPads to supervise production.

In addition, multinational companies such as Shell and Fujitsu are also taking action in this field.

CCID Consulting | 2019 Sensor Market Data (Forward)

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