Impact of Selector Devices in Analog RRAM-Based Crossbar Arrays for Inference and Training of Neuromorphic System

Abstract

The impact of selector devices on the inference and training accuracy of a resistive random access memory (RRAM)-based neuromorphic computing system is rarely studied. In this paper, we analyze the weighted sum and weight update functions in a one-selector–one-RRAM (1S–1R)-based crossbar arrays. We first develop a Verilog-A model based on the lateral evolution of the filament to describe analog conductance tuning in the filamentary RRAM. We then perform an array-level SPICE simulation on the 1S–1R arrays, where the exponential and threshold selectors are employed. In the inference stage, the read-out current is vulnerable to the inevitable IR drop caused by the wire resistance. Our finding reveals that the use of a threshold selector allows the 1S–1R device to have a linear I–V relation, improving the immunity to the IR drop. On the other hand, the threshold selector distorts analog RRAM’s linear weight update during the training stage. Instead, an introduction of exponential selector enables the desirable properties of analog RRAM to be maintained even in the 1S–1R device. These results indicate that different selectors suitable for each operation mode (inference or training) are preferred in the neuromorphic computing system.