Two-Terminal MoS<sub>2</sub> Memristor and the Homogeneous Integration with a MoS<sub>2</sub> Transistor for Neural Networks
Shuai Fu, Ji Hoon Park, Hongyan Gao, Tianyi Zhang, Xiang Ji, Tianda Fu, Lu Sun, Jing Kong, Jun Yao
Abstract
Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS 2 memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS 2 transistors to realize one-transistor–one-memristor addressable cells for assembling programmable networks. The homogenously integrated cells are implemented in a 2 × 2 network array to demonstrate the enabled addressability and programmability. The potential for assembling a scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems.