College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, People's Republic of China.
Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, People's Republic of China.
ACS Nano. 2022 Dec 27;16(12):21324-21333. doi: 10.1021/acsnano.2c09569. Epub 2022 Dec 15.
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.
储层计算 (RC) 是一种能够有效处理时间信息的计算架构，允许低成本的硬件实现。然而，以前报道的基于忆阻器的 RC 大多利用二值数据集来降低忆阻器信号处理的难度，这不可避免地在一定程度上导致数据失真，从而导致网络计算性能不佳。在这里，我们报告了一个完全基于溶液处理钙钛矿忆阻器的 RC 系统。钙钛矿忆阻器具有 10000 个电导状态，调制范围超过 4 个数量级。获得的数万个具有近乎理想的模拟特性的精细间隔电导状态提供了足够大的动态范围和足够的中间状态，这些状态进一步作为储层，以模拟方式映射不同顺序输入的特征信息。对 Fashion-MNIST 数据集的图像分类任务的计算能力具有高达 90.1%的高识别精度，表明我们的钙钛矿忆阻器具有优异的模拟和短期特性，允许以降低的训练成本实现神经形态计算的硬件实现。
