Memristors with programmable conductance are thought of promising for energy-efficient analog reminiscence and neuromorphic computing in edge AI methods. To enhance reminiscence density and computational effectivity, attaining a number of secure conductance states inside a single gadget is especially essential. On this work, we exhibit multilevel conductance tuning in few-layer tin hexathiophosphate (SnP2S6, SPS) memristors, attaining 325 secure states by way of a pulse-based programming scheme. By analyzing conductive filament evolution, we devised a voltage-pulse strategy that successfully suppresses present noise, thereby maximizing the variety of distinguishable states inside the gadget ON/OFF ratio. Moreover, we experimentally emulated synaptic plasticity behaviors together with long-term potentiation and despair, and validated their efficiency by way of synthetic neural community simulations on digit classification. These outcomes spotlight the potential of SPS memristors as high-resolution analog reminiscence and as constructing blocks for neuromorphic computing, providing a pathway towards compact and environment friendly architectures for next-generation edge intelligence.
