Suppr超能文献

用于深度神经网络的具有光诱导硫醇-烯交联聚合物电解质的有机固态电解质突触晶体管

Organic Solid-State Electrolyte Synaptic Transistors with Photoinduced Thiol-Ene Cross-linked Polymer Electrolytes for Deep Neural Networks.

作者信息

Chen Qun-Gao, Liao Wei-Ting, Li Rou-Yi, Sanjuán Ignacio, Hsiao Ning-Cian, Ng Chan-Tat, Chang Ting-Ting, Guerrero Antonio, Chueh Chu-Chen, Lee Wen-Ya

机构信息

Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei 106344, Taiwan.

Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain.

出版信息

ACS Mater Lett. 2025 Jan 23;7(2):682-691. doi: 10.1021/acsmaterialslett.4c02511. eCollection 2025 Feb 3.

Abstract

In this work, we describe a solid-state polymer electrolyte (SPE)-based electrolyte-gated organic field-effect transistors (EGOFETs) consisting of a thiol-ene-assisted photo-cross-linked nitrile butadiene rubber (NBR) network embedded with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) electrolyte. The photocurable SPE film can be patterned with different dimensions by photolithography and exhibits excellent electronic properties and crucial synaptic behavior. The photocurable NBR/LiTFSI EGOFET exhibits a high transconductance of 11.9 mS and a high on/off ratio of 10 at a scan rate of 40 mV/s. Due to the strongly polarized nature of the photo-cross-linked NBR network and Li-ion diffusion, the NBR/LiTFSI device exhibits a significant current hysteresis, enabling synaptic-like learning and memory behavior. The NBR/LiTFSI device demonstrates a DNN of 91.9% handwritten digit recognition accuracy. This work demonstrates the potential of the solid-state NBR/LiTFSI EGOFET in creating highly efficient and low-energy neuromorphic devices.

摘要

在这项工作中,我们描述了一种基于固态聚合物电解质(SPE)的电解质门控有机场效应晶体管(EGOFET),它由嵌入双(三氟甲磺酰)亚胺锂(LiTFSI)电解质的硫醇-烯辅助光交联丁腈橡胶(NBR)网络组成。通过光刻技术,可将光固化的SPE膜制作成不同尺寸的图案,并且该膜展现出优异的电学性能和关键的突触行为。在40 mV/s的扫描速率下,光固化的NBR/LiTFSI EGOFET表现出11.9 mS的高跨导和10的高开关比。由于光交联NBR网络的强极化特性以及锂离子扩散,NBR/LiTFSI器件表现出显著的电流滞后现象,从而实现类似突触的学习和记忆行为。NBR/LiTFSI器件在手写数字识别准确率方面达到了91.9%的深度神经网络(DNN)精度。这项工作展示了固态NBR/LiTFSI EGOFET在创建高效且低能耗的神经形态器件方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecc5/11795873/7864e2c7f319/tz4c02511_0001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验