TY  - JOUR
AU  - Xie, Zhiqiang
AU  - Wu, Jianchang
AU  - Tian, Jingjing
AU  - Li, Chaohui
AU  - Zhang, Difei
AU  - Chen, Lijun
AU  - Loi, Maria Antonietta
AU  - Osvet, Andres
AU  - Brabec, Christoph
TI  - Additive-Engineered CsPbBr 3 -Based Perovskite Memristors for Neuromorphic Computing and Associative Learning Applications
JO  - ACS applied materials & interfaces
VL  - 17
IS  - 38
SN  - 1944-8244
CY  - Washington, DC
PB  - Soc.
M1  - FZJ-2025-04184
SP  - 53704 - 53715
PY  - 2025
AB  - Perovskite memristors have emerged as promising candidates for neuromorphic computing due to their simple fabrication process and mixed ionic and electronic properties. Among them, all-inorganic CsPbBr3 perovskites have garnered significant interest due to their excellent stability. However, the low solubility of cesium bromide (CsBr) in most common solvents poses a major challenge in fabricating high-quality, pinhole-free CsPbBr3 films for memory device applications using a convenient one-step solution method. In this work, a facile one-step spin-coating approach was employed to fabricate CsPbBr3-based memristors, incorporating a carbohydrazide (CBH) additive into the perovskite precursor to enhance device performance. The modified device exhibited an improved ON/OFF ratio, enhanced endurance, and longer retention time. Furthermore, it successfully emulated key synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, and learning–forgetting–relearning behaviors, effectively mimicking biological synapses. Additionally, an associative learning experiment inspired by Pavlov’s dog experiment was conducted, demonstrating memory formation and extinction under optical and electrical stimuli. The fabricated perovskite memristor was further evaluated in a convolutional neural network for Fashion MNIST classification, achieving a high recognition accuracy of 89.07%, confirming its potential for neuromorphic computing applications. This study highlights the effectiveness of additive engineering as a strategy for developing high-performance perovskite-based neuromorphic electronics.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1021/acsami.5c10525
UR  - https://juser.fz-juelich.de/record/1047253
ER  -