Implementation of Multiply Accumulate Operation and Convolutional Neural Network Based on Ferroelectric Tunnel Junction Memristors

Abstract

In the era of big data, traditional Von Neumann computers suffer from inefficiencies in terms of energy consumption and speed associated with data transfer between storage and processing. In-memory computing using ferroelectric tunnel junction (FTJ) memristors offers a potential solution to this challenge. Here, Hf_0.5Zr_0.5O₂-based FTJs on a silicon substrate are fabricated, which demonstrates 32 conductance states (5-bit), low cycle-to-cycle variation (1.6%) and highly linear (nonlinearity <1) conductance manipulation. Based on an FTJ array with multiple FTJ devices, a custom-designed board with a field programmable gate array is utilized to perform accurate multiply accumulate operations and for image processing as various convolution operators. Notably, using FTJ devices as a convolutional layer, the convolutional neural network achieves a high accuracy of 92.5% for handwritten digit recognition, and exhibits orders of magnitude better energy efficiency compared to traditional CPU and GPU implementations. These findings highlight the promising potential of FTJs for realizing in-memory computing at the hardware level.