Superlattice-like Ge2Sb2Te5/Sb2S3 based phase-change memory enabling linear conductance modulation for neuromorphic computing

Abstract

Artificial synaptic devices that emulate biological synaptic behavior have garnered significant research interest for their potential in enabling efficient and low-power computing architectures. Phase-change memory (PCM) has emerged as a promising candidate for artificial synaptic devices, owing to its non-volatility, high speed, and low-power consumption. However, the inherent abrupt and hard-to-control resistance switching in PCM impedes linear and continuous conductance modulation, which substantially restricts the performance of PCM-based synaptic devices. This work demonstrates an electronic synaptic device fabricated from a Ge2Sb2Te5/Sb2S3 superlattice-like (SLL) phase-change thin film. The unique SLL structure effectively suppresses the rapid crystallization and resistance drift. The device operates at a driving voltage below 1 V with low-power consumption and exhibits eight stable resistance states. It achieves a notably low resistance drift coefficient of 0.0006, which remains stable for over 1000 s. Moreover, through a tailored programming strategy, the device successfully implements synaptic weight updates via long-term potentiation and long-term depression, thereby alleviating the nonlinearity and asymmetry commonly observed in PCM-based conductance modulation. In a handwritten digit recognition task, the device enabled a recognition accuracy of around 97.6%, highlighting its potential for enhancing the precision and reliability of neuromorphic computing systems.