Read-disturb Detection Methodology for RRAM-based Computation-in-Memory Architecture

Abstract

Resistive random access memory (RRAM) based computation-in-memory (CIM) architectures can meet the unprecedented energy efficiency requirements to execute AI algorithms directly on edge devices. However, the read-disturb problem associated with these architectures can lead to accumulated computational errors. To achieve the necessary level of computational accuracy, after a specific number of read cycles, these devices must undergo a reprogramming process which is a static approach and needs a large counter. This paper proposes a circuit-level RRAM read-disturb detection technique by monitoring real-time conductance drifts of RRAM devices, which initiate the reprogramming when actually it needs. Moreover, an analytic method is presented to determine the minimum conductance detection requirements, and our proposed read-disturb detection technique is tuned for the same to detect it dynamically. SPICE simulation result using TSMC 40 nm shows the correct functionality of our proposed detection technique.