Synaptic devices are necessary to meet the growing demand for the smarter and more efficient system. In this work, the anisotropic rhenium disulfide (ReS2) is used as a channel material to construct a synaptic device and successfully emulate the long-term potentiation/depression behavior. To demonstrate that our device can be used in a large-scale neural network system, 165 pictures from Yale Face database are selected for evaluation, of which 120 pictures are used for artificial neural network (ANN) training, and the remaining 45 pictures are used for ANN testing. A three-layer ANN containing more than 105 weights is proposed for the face recognition task. Also 120 continuous modulated conductance states are selected to replace weights in our well-trained ANN. The results show that an excellent recognition rate of 100% is achieved with only 120 conductance states, which proves a high potential of our device in the artificial neural network field.

中文翻译：

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用于面部识别应用的ReS2电荷陷阱突触设备。

突触设备对于满足对更智能，更高效系统的不断增长的需求是必要的。在这项工作中，各向异性的二硫化rh（ReS2）被用作通道材料来构建突触设备并成功模拟长期的增强/抑制行为。为了证明我们的设备可用于大型神经网络系统，从Yale Face数据库中选择了165张图片进行评估，其中120张图片用于人工神经网络（ANN）训练，其余45张图片被使用用于ANN测试。提出了一种三层人工神经网络，包含超过105个权重，用于人脸识别任务。在我们训练有素的人工神经网络中，还选择了120个连续调制电导状态来代替权重。