Tunable Resistive Switching in CsPbBr 3 Nanocrystal‐Based Memristors for Artificial Synapse and Neuromorphic Applications

作者：Subham Saha, Baidyanath Roy, Tamal Dey, Chirantan Ganguly, James Bullock, Ranjith Rajasekharan Unnithan, S. K. Ray · 发表于：Advanced Materials Technologies · 年份：2025 · DOI：10.1002/admt.202500720 · 被引用次数：5 · 研究领域：Advanced Memory and Neural Computing、Perovskite Materials and Applications、Photoreceptor and optogenetics research

Abstract The growing demand for energy‐efficient, brain‐inspired computing has driven interest in memristors for neuromorphic hardware. All‐inorganic halide perovskite cesium lead bromide (CsPbBr 3 ) is a promising material for memristor‐based artificial synapses due to its mixed ionic‐electronic conductivity, low activation energy of bromide vacancy, and superior defect tolerance. This study demonstrates tunable resistive switching properties of a forming‐free memristor with CsPbBr 3 nanocrystals, achieving both digital (abrupt) and analog (gradual) switching for neuromorphic applications. The fabricated device exhibits stable non‐volatile digital switching behavior with an ON/OFF ratio of 10 3 , endurance of 500 cycles, and a high retention time of 4000 s with relatively low SET and RESET voltage, along with displaying gradual conductance states with appropriate voltage pulses. The device replicates various key biological synaptic functionalities, including short‐term plasticity, long‐term plasticity, and paired‐pulse facilitation, spike rate‐dependent plasticity, which can be controlled by the amplitude and duration of the applied bias. The potentiation and depression characteristics are utilized to train an artificial neural network, achieving 93.2% classification accuracy for handwritten digit recognition. This work highlights a reliable method to control switching dynamics in CsPbBr 3 nanocrystal‐based memristors, making them suitable for data storage and in‐memory comp...