In-Memory Computing Enabled by 32 × 8 1T1R Memristive Crossbar Arrays

Abstract

Computing in memory represented by resistive switching (memristive) devices is a promising way to implement hardware for artificial neural networks. We see it as the way to create energy-efficient and high-performance systems for solving a wide range of tasks in artificial intelligence. Here, we present the structure and performance of our crossbar array based on Au/Ta/ZrO2(Y)/Pt/Ti memristive devices, integrated into a complementary metal–oxide–semiconductor fabrication process, and then demonstrate its applicability for the hardware implementation of artificial neural networks. The array contains 256 memristive devices combined into 32 columns and eight rows. Each memristive device is paired with an n-channel metal–oxide–semiconductor transistor. As the results of the simulations show, these devices enable running neural network algorithms for solving recognition problems using the Modified National Institute of Standards and Technology dataset with 98% accuracy and the Canadian Institute for Advanced Research (10 classes) dataset with at least 77.5% accuracy, even taking into account the errors of mapping weights onto the memristive crossbar. Fully hardware recognition of 4 bit images demonstrated in this work highlights the crucial role of selective transistors for such tasks. This paper explores the exciting potential of memristor-based components of neuroelectronics as the building blocks for a new era of artificial intelligence hardware, capable of surpassing current computational limitations, as well as their role in the ongoing transition to hybrid intelligence based on the symbiosis of electronic and biological systems.