Energy-efficient and reconfigurable complementary filter based on analog–digital hybrid computing with SnS2 memtransistor

Abstract

Sensor fusion is a widely exploited technique that combines data from two or more sensors to improve the accuracy to a level that cannot be achieved using a single sensor alone. Algorithms for sensor fusion are generally executed on conventional digital computing platforms; however, these algorithms impose a burden on small electrical systems with limited battery capacities and computing resources. In this study, we demonstrated an analog–digital hybrid computing platform based on an SnS2 memtransistor for energy-efficient and reconfigurable sensor fusion with a complementary filter algorithm. We experimentally verified that the power consumption of our hybrid computing-based complementary filter is only half that of the traditional software-based complementary filter, even with the same accuracy.