Quasi-Volatile MoS<sub>2</sub> Barristor Memory for 1T Compact Neuron by Correlative Charges Trapping and Schottky Barrier Modulation
Jiali Huo, Huaxiang Yin, Yadong Zhang, Xiaosi Tan, Yunwei Mao, Chuan Zhang, Fan Zhang, Guohui Zhan, Zhaohao Zhang, Qingzhu Zhang, Gaobo Xu, Zhenhua Wu
Abstract
Artificial neurons as the basic units of spiking neural network (SNN) have attracted increasing interest in energy-efficient neuromorphic computing. 2D transition metal dichalcogenide (TMD)-based devices have great potential for high-performance and low-power artificial neural devices, owing to their unique ion motion, interface engineering, and resistive switching behaviors. Although there are widespread applications of TMD-based artificial synapses in neural networks, TMD-based neurons are seldom reported due to the lack of bio-plausible multi-mechanisms to mimic leaking, integrating, and firing biological behaviors without external assistance. In this work, for the first time, a methodology is developed by introducing the hybrid effect of charge trapping (CT) and Schottky barrier (SB) in MoS2 FETs for barristor memory and one-transistor (1T) compact artificial neuron realization. By correlating the CT and SB processes, quasi-volatile and resistive switching behaviors are realized on the fabricated MoS2 FET and utilized to mimic the accumulating, leaking, and firing biological behaviors of neurons. Therefore, based on a single quasi-volatile CT-SB MoS2 barristor memory, a 1T compact neuron of the basic leaky-integral-and-fire (LIF) function is demonstrated without a peripheral circuit. Furthermore, a spiking neural network (SNN) based on the CT-SB MoS2 barristor neurons is simulated and implemented in pattern classification with high accuracy approaching 95.82%. This work provides a highly integrated and inherently low-energy implementation for neural networks.