Self-Powered Photonic Synapses with Rapid Optical Erasing Ability for Neuromorphic Visual Perception

被引:2
|
作者
Li, Mingchao [1 ]
Li, Chen [2 ]
Ye, Kang [1 ]
Xu, Yunzhe [2 ]
Song, Weichen [2 ]
Liu, Cihui [1 ]
Xing, Fangjian [1 ]
Cao, Guiyuan [3 ]
Wei, Shibiao [3 ]
Chen, Zhihui [4 ]
Di, Yunsong [1 ]
Gan, Zhixing [1 ]
机构
[1] Nanjing Normal Univ, Ctr Future Optoelect Funct Mat, Sch Comp & Elect Informat, Sch Artificial Intelligence, Nanjing 210023, Peoples R China
[2] Southeast Univ, Sch Elect Sci & Engn, Joint Int Res Lab Informat Display & Visualizat, Nanjing 210096, Peoples R China
[3] Shenzhen Univ, Nanophoton Res Ctr, Shenzhen Key Lab Microscale Opt Informat Technol, Shenzhen 518060, Peoples R China
[4] Taiyuan Univ Technol, Coll Elect Informat & Opt Engn, Key Lab Adv Transducers & Intelligent Control Syst, Minist Educ & Shanxi Prov, Taiyuan 030024, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Compendex;
D O I
10.34133/research.0526
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Photonic synapses combining photosensitivity and synaptic function can efficiently perceive and memorize visual information, making them crucial for the development of artificial vision systems. However, the development of high-performance photonic synapses with low power consumption and rapid optical erasing ability remains challenging. Here, we propose a photon-modulated charging/discharging mechanism for self-powered photonic synapses. The current hysteresis enables the devices based on CsPbBr3/solvent/carbon nitride multilayer architecture to emulate synaptic behaviors, such as excitatory postsynaptic currents, paired-pulse facilitation, and long/short-term memory. Intriguingly, the unique radiation direction-dependent photocurrent endows the photonic synapses with the capability of optical writing and rapid optical erasing. Moreover, the photonic synapses exhibit exceptional performance in contrast enhancement and noise reduction owing to the notable synaptic plasticity. In simulations based on artificial neural network (ANN) algorithms, the pre-processing by our photonic synapses improves the recognition rate of handwritten digit from 11.4% (200 training epochs) to 85% (similar to 60 training epochs). Furthermore, due to the excellent feature extraction and memory capability, an array based on the photonic synapses can imitate facial recognition of human retina without the assistance of ANN.
引用
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页数:11
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