Energy-efficient and reconfigurable complementary filter based on analog–digital hybrid computing with SnS2 memtransistor. (May 2023)

• Record Type: Journal Article
• Authors: Rehman, Shania; Khan, Muhammad Farooq; Kim, Hee-Dong; Kim, Sungho
• Is Part Of: Nano energy. Volume 109(2023)
• Publication Date: 2023-05
• Subjects: Analog-digital hybrid circuit -- Drone -- Memtransistor -- Sensor fusion -- Tin disulfide; Nanoscience -- Periodicals; Nanotechnology -- Periodicals; Nanostructured materials -- Periodicals; Power resources -- Technological innovations -- Periodicals; Nanoscience; Nanostructured materials; Nanotechnology; Power resources -- Technological innovations; Periodicals; 621.042
• DOI: 10.1016/j.nanoen.2023.108333
• Languages: English
• ISSNs: 2211-2855

Abstract

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. Graphical Abstract: ga1 Highlights: Analog-digital hybrid computing for low-power and reconfigurable sensor fusion in drones. SnS2 memtransistor-based analog circuit execute a complementary filter algorithm energy-efficiently. Precisely tunable conductance state of the SnS2 memtransistor enables a reconfigurable performance. Mechanism of highly reliable analog resistive switching behavior in the SnS2 memtransistor.