Differential Phase Change Memory (PCM) Cell for Drift-Compensated In-Memory Computing

Phase change memory (PCM) is a scalable, reliable, and robust technology for embedded and stand-alone memory device. PCM has also been extensively demonstrated for analog in-memory computing (IMC), which allows energy-efficient acceleration of AI workloads. High-temperature data retention requirements in PCM devices are met by Ge-rich GeSbTe (GST), which allows to satisfy consumer-and automotive-grade reliability specifications. However, Ge-rich GST suffers from set state drift, which affects the stability of the multilevel cell (MLC), hence the accuracy of IMC. This work presents: 1) a novel multilevel programming algorithm from weak reset state to prevent conductance instabilities; 2) a drift compensation scheme through a differential weight approach, validated on matrix-vector multiplication (MVM) after high-temperature annealing; and 3) a detailed study of the impact of PCM variability and weight quantization on hardware implementation of neural networks.