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

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.