用二维材料构建存算一体的神经网络，识别语音数字准确率达89.56%
Interfacial and bulk switching MoS2 memristors for an all-2D reservoir computing framework
- 通过调控二硫化钼厚度，实现挥发性与非挥发性两种记忆态
- 4位储层状态+非挥发突触阵列，语音识别准确率达89.56%
- 适合低功耗边缘计算与时间序列建模场景
本研究利用金/钛/化学气相沉积二硫化钼/金忆阻器件中的短时与长时记忆动态，构建了储层计算（RC）网络。通过控制二硫化钼薄膜厚度，单层（1L）MoS₂器件表现出挥发性（短时记忆）开关特性；多层（ML）MoS₂器件则呈现优异均匀性与模拟导电调节的非挥发性电阻开关行为。该性能归因于陷阱辅助空间电荷限制导电（SCLC）机制，导致体相受限的电阻开关。采用挥发性忆阻器生成四位储层状态，读出层由非挥发性突触阵列实现。该小型RC网络在语音数字识别任务中达到89.56%精度，并成功用于分析非线性时间序列方程。
原文摘要 · Abstract (English)
In this study, we design a reservoir computing (RC) network by exploiting short- and long-term memory dynamics in Au/Ti/MoS$_2$/Au memristive devices. The temporal dynamics is engineered by controlling the thickness of the Chemical Vapor Deposited (CVD) MoS$_2$ films. Devices with a monolayer (1L)-MoS$_2$ film exhibit volatile (short-term memory) switching dynamics. We also report non-volatile resistance switching with excellent uniformity and analog behavior in conductance tuning for the multilayer (ML) MoS$_2$ memristive devices. We correlate this performance with trap-assisted space-charge limited conduction (SCLC) mechanism, leading to a bulk-limited resistance switching behavior. Four-bit reservoir states are generated using volatile memristors. The readout layer is implemented with an array of nonvolatile synapses. This small RC network achieves 89.56\% precision in a spoken-digit recognition task and is also used to analyze a nonlinear time series equation.
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