ScholarWorks@경상국립대학교

초록

Neuromorphic computing addresses the von Neumann bottleneck by integrating memory and processing to emulate synaptic behavior. Artificial synapses enable this functionality through analog conductance modulation, low-power operation, and nanoscale integration. Halide perovskites with high ionic mobilities and solution processabilities have emerged as promising materials for such devices; however, inherent stochastic ion migration and thermal instability lead to asymmetric and nonlinear characteristics, ultimately impairing their learning and inference capabilities. To overcome these limitations, this study introduces a polyvinyl alcohol (PVA)-based hydrogen-bonding interface engineering strategy to stabilize CsPbI3 artificial synapses. Density functional theory calculations and experimental analyses indicate that the hydroxyl groups in PVA form robust O─H···I− bonds with surface iodides, promoting vertical lattice ordering. This suppresses grain boundary defects and enables directional ion migration, resulting in extremely linear and symmetric optoelectronic conductance modulation (αp = 0.004, αd = 0.020), over eight fold reduction in interfacial trap density, and high-temperature retention (>104 s). When integrated into a neural network, artificial synapses show large-scale image classification accuracy within 1.62% of the theoretical limit. The proposed strategy provides a scalable pathway toward overcoming the existing limitations of artificial synapses, exhibiting high potential for application in edge AI, autonomous systems, and material-based cognitive modeling.

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