神经形态工程学
材料科学
电容
神经科学
长时程增强
电导
人工神经网络
电容器
突触可塑性
计算机科学
人工智能
物理
电极
电气工程
电压
生物
工程类
生物化学
量子力学
凝聚态物理
受体
作者
Kuan‐Ting Chen,Li‐Chung Shih,Shi‐Cheng Mao,Jen‐Sue Chen
标识
DOI:10.1021/acsami.2c20297
摘要
Neuromorphic computing, inspired by the biological neuronal system, is a high potential approach to substantially alleviate the cost of computational latency and energy for massive data processing. Artificial synapses with regulable synaptic weights are the basis of neuromorphic computation, providing an efficient and low-power system to overcome the constraints of the von Neumann architecture. Here, we report an ITO/TaOx-based synaptic capacitor and transistor. With the drift motion of mobile-charged ions in the TaOx, the capacitance and channel conductance can be tuned to exhibit synaptic weight modulation. Robust stability in the cycle-to-cycle (C2C) variation is found in capacitance and conductance potentiation/depression weight updating of 0.9 and 1.8%, respectively. Simulation results show a higher classification accuracy of handwritten digit recognition (95%) in capacitance synapses than that in conductance synapses (84%). Besides, the synaptic capacitor consumes much less energy than the synaptic transistor. Moreover, the ITO/TaOx-based capacitor successfully emulates the pain-perceptual sensitization on top of the superior performance, indicating its promising potential in applying the capacitive neural network.
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