A New Insight and Modeling of Pulse-to-Pulse Variability in Analog Resistive Memory for On-Chip Training

Abstract

Pulse-to-pulse variability (PPV) is a vital nonideal effect in analog resistive memory, particularly for online training in emerging computing paradigms. The reported PPVs usually show two seemingly contradictory properties: large PPV amplitude between neighboring write pulses but statistically stable switching curves. In this article, a new insight is proposed to unambiguously decouple PPV into two independent components: variability and curve fluctuation. Furthermore, a phenomenological modeling and parameter extraction method is proposed to capture this characteristic. The proposed model can facilitate the exploration of PPV impact on neural network accuracy for online training.