Investigation of the Versatile Utilization of Three-Dimensional Vertical Resistive Random-Access Memory in Neuromorphic Computing

Abstract

The continuous advancement of computing technologies such as the Internet of Things and artificial intelligence emphasizes the need for innovative approaches to data processing. Faced with limitations in processing speed due to the exponentially increasing volume of data, conventional von-Neumann computing systems are transforming into a new paradigm called neuromorphic computing, inspired by the efficiency of the human brain. We fabricate three-dimensional vertical resistive random-access memory (VRRAM), which is highly suited for neuromorphic computing, and demonstrate its value as an artificial synapse. Beyond simulating simple synaptic functionalities such as spike-rate-dependent plasticity, spike-timing-dependent plasticity, and paired-pulse facilitation, we propose specific applications and experimentally implement them. In pattern recognition simulations based on the weight update characteristics of the fabricated VRRAM, the accuracy achieved in pattern recognition using appropriate pulse schemes reaches 90.4%. Additionally, we demonstrate adaptive learning behavior on the device by mimicking Pavlov's dog experiment with combinations of applied voltage pulses. Finally, we employ suitable write/erase pulse trains to implement binary representations for decimal numbers ranging from 0 to 15, thereby illustrating the significant potential of local devices for edge computing applications.