典型文献
Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices
文献摘要：
High-accuracy neuromorphic devices with adaptive weight adjustment are crucial for high-performance computing.However,limited studies have been conducted on achieving selective and linear synaptic weight updates without changing electrical pulses.Herein,we propose high-accuracy and self-adaptive artificial synapses based on tunable and flexible MXene energy storage devices.These synapses can be adjusted adaptively depending on the stored weight value to mitigate time and energy loss resulting from recalculation.The resistance can be used to effectively regulate the accumulation and dissipation of ions in single devices,without changing the external pulse stimulation or preprogramming,to ensure selective and linear synaptic weight updates.The feasibility of the proposed neural network based on the synapses of flexible energy devices was investigated through training and machine learning.The results indicated that the device achieved a recognition accuracy of 95％for various neural network calculation tasks such as numeric classification.
文献关键词：
中图分类号：
作者姓名：
Shufang Zhao;Wenhao Ran;Zheng Lou;Linlin Li;Swapnadeep Poddar;Lili Wang;Zhiyong Fan;Guozhen Shen
作者机构：
State Key Laboratory for Superlattices and Microstructures,Institute of Semiconductors,Chinese Academy of Sciences,and Center of Materials Science and Optoelectronic Engineering,University of Chinese Academy of Sciences,Beijing 100083 China;Department of Electronic and Computer Engineering,The Hong Kong University of Science and Technology,Hong Kong,China;School of Integrated Circuits and Electronics,Beijing Institute of Technology,Beijing 100081,China
文献出处：
引用格式：
[1]Shufang Zhao;Wenhao Ran;Zheng Lou;Linlin Li;Swapnadeep Poddar;Lili Wang;Zhiyong Fan;Guozhen Shen-.Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices)[J].国家科学评论（英文版）,2022(11):167-176
A类：
recalculation,preprogramming
B类：
Neuromorphic,computing,learning,using,dynamics,flexible,energy,storage,devices,High,accuracy,neuromorphic,weight,adjustment,are,crucial,high,performance,However,limited,studies,have,been,conducted,achieving,selective,linear,synaptic,updates,without,changing,electrical,pulses,Herein,self,artificial,synapses,tunable,MXene,These,can,adjusted,adaptively,depending,stored,value,mitigate,loss,resulting,from,resistance,used,effectively,regulate,accumulation,dissipation,ions,single,external,stimulation,ensure,feasibility,proposed,neural,network,was,investigated,through,training,machine,results,indicated,that,achieved,recognition,various,tasks,such,numeric,classification
AB值：
0.560041
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
