Details

Autor(en) / Beteiligte

• Yang, Jiaqin
• Zhang, Fan
• Xiao, Hao-Min
• Wang, Zhan-Peng
• Xie, Peng
• Feng, Zihao
• Wang, Junjie
• Mao, Jingyu
• Zhou, Ye
• Han, Su-Ting

Titel

A Perovskite Memristor with Large Dynamic Space for Analog-Encoded Image Recognition

Ist Teil von

• ACS nano, 2022-12, Vol.16 (12), p.21324-21333

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Reservoir computing (RC) is a computational architecture capable of efficiently processing temporal information, which allows low-cost hardware implementation. However, the previously reported memristor-based RC mostly utilized binarized data sets to reduce the difficulty of signal processing of the memristor, which inevitably induces data distortion to a certain extent, leading to poor network computing performance. Here, we report on a RC system in a fully memristive architecture based on solution-processed perovskite memristors. The perovskite memristor exhibits 10000 conductance states with a modulation range of more than 4 orders of magnitude. The obtained tens of thousands of finely spaced conductance states with a near-ideal analog property provide a sufficiently large dynamic range and enough intermediate states, which were further applied as a reservoir to map the feature information on different sequential inputs in an analog way. The computing capability of the image classification task of a Fashion-MNIST data set with a high recognition accuracy of up to 90.1% shows that the excellent analog and short-term properties of our perovskite memristor allow the hardware implementation of neuromorphic computing with a reduced training cost.