Field-driven modulating of In-Sn-O synaptic transistors with a precisely controlled weight update. (June 2021)

• Record Type: Journal Article
• Authors: Liu, Wanrong; Huang, Yulong; Jin, Chenxing; Zhou, Bosheng; Yu, Jinran; Liang, Yuchen; Sun, Qijun; Sun, Jia; Yang, Junliang
• Is Part Of: Applied materials today. Volume 23(2021)
• Publication Date: 2021-06
• Subjects: Synaptic devices -- In-Sn-O memtransistors -- Steep slope -- Precise weight update; Materials science -- Periodicals; Materials -- Research -- Periodicals; 620.1105
• DOI: 10.1016/j.apmt.2021.101024
• Languages: English
• ISSNs: 2352-9407

Abstract

Highlights: A multi-gate structure is designed to explore the influence of electric field on the generation/removal of oxygen vacancies in In-Sn-O memtransistors. A novel weight update method is proposed for simulating of artificial neural networks with a high accuracy of 90%. Abstract: Multi-gate architectures in synaptic transistor are promising to implement the modulation on transport properties of channel and electrical performances of device. Here, we demonstrate a steep-slope In-Sn-O memtransistor with multi-gate design to simulate the synaptic functions with readily programmable plasticity. The working mechanism of field-driven modulating oxygen vacancies in In-Sn-O channel has been elaborately explored. Furthermore, artificial neutral networks (ANNs) are simulated by constructing the In-Sn-O memtransistors in a crossbar array, and 3528 statistical data points for long-term potentiation are gathered from the memtransistors to explore the regularity of the conductance state. An efficient algorithm is developed to precisely control the weight update, which is used to construct an XOR gate function. The simulated ANNs for image recognition training with the Modified National Institute of Standards and Technology (MNIST) dataset have also been achieved. The recognition accuracy of the simulation can reach as high as 90%. The proposed weight update method provides a new strategy for developing neuromorphic computing. Graphical abstract: Image, graphical abstract