Yujiao Li , Shanshan Jiang , Bo He , Bingyan Wang , Jiawei Yang , Huanhuan Wei , Can Fu , Gang He
{"title":"High-performance organic synaptic transistors for multi-wavelength perception simulation and neuromorphic computing","authors":"Yujiao Li , Shanshan Jiang , Bo He , Bingyan Wang , Jiawei Yang , Huanhuan Wei , Can Fu , Gang He","doi":"10.1016/j.mtelec.2025.100164","DOIUrl":null,"url":null,"abstract":"<div><div>Inspired by biological neuromorphic systems, biomimetic artificial synaptic devices based on organic transistors have become a prominent research direction. The polymer PDVT-10, which is commonly used as channel layer in devices, has excellent stability and high mobility. However, previous research in simulating photonic synapses has focused on doping and hybrid structures, which is limited by the choice of materials and complex fabrication processes in exploring the multifunctional applications of photonic synaptic devices in the future. Here, we successfully constructed individual PDVT-10 photoelectric synaptic devices to simulate biological synaptic plasticity under different wavelengths of light pulse stimulation for the first time. Furthermore, the application of light-induced high-pass filtering characteristics, pain sensing, sensitization, as well as memory functions were realized. In addition, the application of logic circuits was realized based on the photoelectric synergistic effect. Moreover, the introduction of a polyvinyl alcohol light-absorbing layer endowed the device with light potentiation and electrical depression function. Subsequently, a simple convolutional neural network was successfully constructed and implemented for the MNIST number recognition task. This work not only contributes to an in-depth understanding of the response mechanism of the device to different wavelengths of light, but also enriches the application areas of the device and provides important support for the practical applications of neuromorphic computing in the future.</div></div>","PeriodicalId":100893,"journal":{"name":"Materials Today Electronics","volume":"13 ","pages":"Article 100164"},"PeriodicalIF":7.4000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Today Electronics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772949425000300","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Inspired by biological neuromorphic systems, biomimetic artificial synaptic devices based on organic transistors have become a prominent research direction. The polymer PDVT-10, which is commonly used as channel layer in devices, has excellent stability and high mobility. However, previous research in simulating photonic synapses has focused on doping and hybrid structures, which is limited by the choice of materials and complex fabrication processes in exploring the multifunctional applications of photonic synaptic devices in the future. Here, we successfully constructed individual PDVT-10 photoelectric synaptic devices to simulate biological synaptic plasticity under different wavelengths of light pulse stimulation for the first time. Furthermore, the application of light-induced high-pass filtering characteristics, pain sensing, sensitization, as well as memory functions were realized. In addition, the application of logic circuits was realized based on the photoelectric synergistic effect. Moreover, the introduction of a polyvinyl alcohol light-absorbing layer endowed the device with light potentiation and electrical depression function. Subsequently, a simple convolutional neural network was successfully constructed and implemented for the MNIST number recognition task. This work not only contributes to an in-depth understanding of the response mechanism of the device to different wavelengths of light, but also enriches the application areas of the device and provides important support for the practical applications of neuromorphic computing in the future.