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Promising guanidinium layered perovskite photoelectric synaptic transistors for neuromorphic computing and image recognition.

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Bibliographic Details
Title:	Promising guanidinium layered perovskite photoelectric synaptic transistors for neuromorphic computing and image recognition.
Authors:	Meng, Jie, Wang, Wenwen, Zhang, Jiangdong, Liang, Yinchun, Tian, Li, Zhao, Jinjin
Source:	Journal of Materials Chemistry C; 11/14/2025, Vol. 13 Issue 42, p21608-21617, 10p
Abstract:	With the rapid development of bio-inspired hardware and artificial neural networks, synaptic transistors have emerged as core components in neuromorphic computing systems. Two-dimensional halide perovskites exhibit significant potential for use in photoelectric synaptic devices owing to their superior photoelectric response characteristics and structural stability. In this study, we propose a two-dimensional (GA)(MA)5Pb5I16 perovskite-based photoelectric synaptic transistor. The device exhibits basic synaptic functions under different light conditions, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), short-term plasticity (STP), and long-term plasticity (LTP), demonstrating inherent learning ability. In addition, it accurately recognizes letters in a 5 × 5 synaptic array and maintains a memory time of over 150 s under a light intensity of 60 mW cm⁻². Finally, using the unique characteristics of photonic potentiation and electric depression, a multi-layer convolutional neural network is constructed to recognize stained blood cell images with an accuracy of 91.55%. This study provides valuable insights for developing neuromorphic systems and advancing artificial intelligence based on two-dimensional perovskite photoelectric synaptic transistors. [ABSTRACT FROM AUTHOR]
	Copyright of Journal of Materials Chemistry C is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Complementary Index

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