Two-Step Read Scheme in One-Selector and One-RRAM Crossbar-Based Neural Network for Improved Inference Robustness

Abstract

Introducing a threshold switching selector in a resistive random access memory (RRAM) is essential for implementing a crossbar array that accurately accelerates neuromorphic computations. But, at an expense, a read voltage ( ${V}_{\text {read}}$ ) to be used for inference tasks is inevitably boosted. Therefore, this brief shows the effect of the enlarged ${V}_{\text {read}}$ on the stability of conductance states of the RRAM relevant to the inference robustness. The multiple conductance states of the analog RRAM achieved by a SPICE simulation are stable under consecutive 106 cycles of nominal ${V}_{\text {read}}$ . However, each state of the one selector and one RRAM begins to be disturbed at ~104 cycles due to the boosted ${V}_{\text {read}}$ . More importantly, when a certain state exceeds to the next state due to the accumulated ${V}_{\text {read}}$ stress, a classification accuracy of the neural network is significantly degraded. We, thus, introduce a two-step read scheme that separates the roles of turning on the selector and reading the states. As the selector is turned on rapidly with an additional large pulse, the following ${V}_{\text {read}}$ can be lowered. As a result, the read disturbance is minimized, and the optimized two-step pulse scheme allows 106 MNIST images to be recognized with >95% accuracy in the neural network.