High Rectification Ratio Self-Rectifying Memristor Crossbar Array for Convolutional Neural Network Operations

Abstract

Oxide-based self-rectifying memristors have emerged as promising candidates for the construction of neural networks, owing to their advantageous features such as high-density integration, low power consumption, 3D stackability, straightforward fabrication processes, and compatibility with complementary metal-oxide-semiconductor (CMOS) technology. Notwithstanding these merits, there remains considerable scope for the suppression of parasitic currents in large-scale memristor arrays, which poses a notable challenge in the development of extensive neural networks capable of executing intricate computational tasks. This study introduces a 1 kbit self-rectifying memristor array based on Pt/HfO₂/Ti structural units. Individual devices in this array not only exhibit switching ratios exceeding 10³, but also maintain rectification ratios greater than 10⁵, and their excellent negative rectification performance effectively suppresses latent path currents in the array. Moreover, the convolutional calculation logic and forward inference process of 8-bit neural networks are demonstrated based on this array, which verifies the feasibility of using arrays to simulate convolutional neural networks for all hardware operations. Ultimately, a complete convolutional neural network system is constructed, the system achieving a recognition rate of up to 98% in the handwriting recognition task. This work provides a new strategy toward the implementation of all-hardware computing for convolutional neural networks.