High-Precision Close-to-Analog Programming of PCM Cells as Devices for AiMC Edge-AI

This article presents a high-precision close-to-analog programming methodology for phase-change memory (PCM) cells, targeting analog in-memory computing (AiMC) architectures for edge-artificial intelligence (AI) applications. By leveraging an iterative accumulation approach during the verify phase, the proposed technique enhances the effective number of bits (ENOBs) achievable in PCM cells. Experimental validation on a 28-nm STMicroelectronics FD-SOI PCM-based AiMC macro demonstrates the capability to accurately program large-scale matrix-vector multiplication (MVM) weights. The results highlight the potential of the proposed programming scheme to overcome intrinsic device variability, resulting in high accuracy in MVM computation. The methodology also shows promising applicability for mapping deep neural network (DNN) and large language model (LLM) weights with high precision, reaching an ENOB of 10.5.