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/HfO ₂ /Ti structural units. Individual devices in this array not only exhibit switching ratios exceeding 10 ³ , but also maintain rectification ratios greater than 10 ⁵ , 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.