Effect of Initial Synaptic State on Pattern Classification Accuracy of 3D Vertical Resistive Random Access Memory (VRRAM) Synapses

Abstract

Amidst the considerable attention artificial intelligence (AI) has attracted in recent years, a neuromorphic chip that mimics the biological neuron has emerged as a promising technology. Memristor or Resistive random-access memory (RRAM) is widely used to implement a synaptic device. Recently, 3D vertical RRAM (VRRAM) has become a promising candidate to reducing resistive memory bit cost. This study investigates the operation principle of synapse in 3D VRRAM architecture. In these devices, the classification response current through a vertical pillar is set by applying a training algorithm to the memristors. The accuracy of neural networks with 3D VRRAM synapses was verified by using the HSPICE simulator to classify the alphabet in 7×7 character images. This simulation demonstrated that 3D VRRAMs are usable as synapses in a neural network system and that a 3D VRRAM synapse should be designed to consider the initial value of the memristor to prepare the training conditions for high classification accuracy. These results mean that a synaptic circuit using 3D VRRAM will become a key technology for implementing neural computing hardware.