28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs

Abstract

Seeking to circumvent conventional computing bottlenecks, hardware alternatives, from brain-inspired designs to cryogenic quantum systems, necessitate integrating emerging non-volatile memories. Yet, the immaturity and unreliability of cryogenic-compatible memories hinder scalable computing advancements. This study characterizes 28 nm FD-SOI substrate-embedded Ge-rich Ge2Sb2Te5 phase change memories (ePCMs) down to 12 K to overcome these hurdles. It reveals that ePCMs is cryogenic compatible and can encode multiple resistance states with minimal drift, essential for advanced computing solutions. Through simulations, the ePCM’s impact on a spiking neural network (SNN) performing MNIST classification is evaluated. The SNN maintains high accuracy for extended periods of 2 years at cryogenic temperatures, while an accuracy drop of 10.8% is observed at room temperature. These results highlight the potential of multilevel ePCMs in brain-inspired cryogenic computing applications, offering a promising avenue for the evolution of unconventional computing systems.