Li Mingchao, Li Chen, Ye Kang, Xu Yunzhe, Song Weichen, Liu Cihui, Xing Fangjian, Cao Guiyuan, Wei Shibiao, Chen Zhihui, Di Yunsong, Gan Zhixing
Center for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing 210023, P. R. China.
Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, P. R. China.
Research (Wash D C). 2024 Nov 7;7:0526. doi: 10.34133/research.0526. eCollection 2024.
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 CsPbBr/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% (~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.
结合光敏性和突触功能的光子突触能够有效地感知和记忆视觉信息,这使其对于人工视觉系统的发展至关重要。然而,开发具有低功耗和快速光擦除能力的高性能光子突触仍然具有挑战性。在此,我们提出一种用于自供电光子突触的光子调制充电/放电机制。电流滞后使得基于CsPbBr/溶剂/氮化碳多层结构的器件能够模拟突触行为,如兴奋性突触后电流、双脉冲易化以及长/短期记忆。有趣的是,独特的辐射方向依赖性光电流赋予光子突触光写入和快速光擦除的能力。此外,由于显著的突触可塑性,光子突触在对比度增强和降噪方面表现出卓越性能。在基于人工神经网络(ANN)算法的模拟中,我们的光子突触进行的预处理将手写数字的识别率从11.4%(200个训练轮次)提高到85%(约60个训练轮次)。此外,由于出色的特征提取和记忆能力,基于光子突触的阵列无需ANN辅助即可模仿人类视网膜的面部识别。