{"title":"Artificial optoelectronic synapse based on CdSe nanobelt photosensitized MoS2 transistor with long retention time for neuromorphic application","authors":"Xiaohui Song, Xiaojing Lv, Mengjie He, Fei Mao, Jie Bai, Xuan Qin, Yanjie Hu, Zinan Ma, Zhen Liu, Xueping Li, Chenhai Shen, Yurong Jiang, Xu Zhao, Congxin Xia","doi":"10.1515/nanoph-2024-0368","DOIUrl":null,"url":null,"abstract":"Optoelectronic synaptic devices have been regarded as the key component in constructing neuromorphic computing systems. However, the optoelectronic synapses based on conventional 2D transistor are still suffering low photosensitivity and volatile retention behavior, which can affect the recognition accuracy and long-term memory. Here, a novel optoelectronic synaptic device based on surface-state-rich CdSe nanobelt photosensitized 2D MoS<jats:sub>2</jats:sub> transistor is demonstrated. Benefiting from the excellent light absorption of CdSe and effective charge trapping at the hetero-interface, the device exhibits not only high photosensitivity but also long retention time (>1,500 s). In addition, typical synaptic functions including the excitatory postsynaptic current, paired-pulse facilitation, the transformation from short-term to long-term plasticity, the transformation from short-term to long-term plasticity, spike-amplitude-dependent plasticity, and learning-forgetting-relearning process are successfully simulated and modulated by light stimulation. Most importantly, an artificial neural network is simulated based on the optical potentiation and electrical habituation characteristics of the synaptic devices, with recognition accuracy rates of 89.2, 93.8, and 91.9 % for file type datasets, small digits, and large digits are achieved. This study demonstrates a simple and efficient way to fabricate highly photosensitive optoelectronic synapse for artificial neural networks by combining the merits of specific materials and device architecture.","PeriodicalId":19027,"journal":{"name":"Nanophotonics","volume":"9 1","pages":""},"PeriodicalIF":6.5000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanophotonics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1515/nanoph-2024-0368","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Optoelectronic synaptic devices have been regarded as the key component in constructing neuromorphic computing systems. However, the optoelectronic synapses based on conventional 2D transistor are still suffering low photosensitivity and volatile retention behavior, which can affect the recognition accuracy and long-term memory. Here, a novel optoelectronic synaptic device based on surface-state-rich CdSe nanobelt photosensitized 2D MoS2 transistor is demonstrated. Benefiting from the excellent light absorption of CdSe and effective charge trapping at the hetero-interface, the device exhibits not only high photosensitivity but also long retention time (>1,500 s). In addition, typical synaptic functions including the excitatory postsynaptic current, paired-pulse facilitation, the transformation from short-term to long-term plasticity, the transformation from short-term to long-term plasticity, spike-amplitude-dependent plasticity, and learning-forgetting-relearning process are successfully simulated and modulated by light stimulation. Most importantly, an artificial neural network is simulated based on the optical potentiation and electrical habituation characteristics of the synaptic devices, with recognition accuracy rates of 89.2, 93.8, and 91.9 % for file type datasets, small digits, and large digits are achieved. This study demonstrates a simple and efficient way to fabricate highly photosensitive optoelectronic synapse for artificial neural networks by combining the merits of specific materials and device architecture.
期刊介绍:
Nanophotonics, published in collaboration with Sciencewise, is a prestigious journal that showcases recent international research results, notable advancements in the field, and innovative applications. It is regarded as one of the leading publications in the realm of nanophotonics and encompasses a range of article types including research articles, selectively invited reviews, letters, and perspectives.
The journal specifically delves into the study of photon interaction with nano-structures, such as carbon nano-tubes, nano metal particles, nano crystals, semiconductor nano dots, photonic crystals, tissue, and DNA. It offers comprehensive coverage of the most up-to-date discoveries, making it an essential resource for physicists, engineers, and material scientists.