Analog–digital hybrid computing with SnS2 memtransistor for low-powered sensor fusion

Shania Rehman, Muhammad Farooq Khan, Hee-Dong Kim and Sungho Kim ()
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Shania Rehman: Sejong University
Muhammad Farooq Khan: Sejong University
Hee-Dong Kim: Sejong University
Sungho Kim: Sejong University

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Algorithms for intelligent drone flights based on sensor fusion are usually implemented using conventional digital computing platforms. However, alternative energy-efficient computing platforms are required for robust flight control in a variety of environments to reduce the burden on both the battery and computing power. In this study, we demonstrated an analog–digital hybrid computing platform based on SnS2 memtransistors for low-power sensor fusion in drones. The analog Kalman filter circuit with memtransistors facilitates noise removal to accurately estimate the rotation of the drone by combining sensing data from the gyroscope and accelerometer. We experimentally verified that the power consumption of our hybrid computing-based Kalman filter is only 1/4th of that of the traditional software-based Kalman filter.

Date: 2022
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DOI: 10.1038/s41467-022-30564-5

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