期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到2篇文章
< 1 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Flexible neuromorphic electronics:from synaptic devices toward sensing-memory-computing circuits 认领 引用 被引量：1
1
作者 An Zhao Yanran Li +4 位作者 Honglin Song Kaiyun Gou Rong Lu Cancan Lu Jie Jiang 《Science China Materials》 SCIE EI CAS CSCD 2026年第4期1862-1897,共36页
In recent years,neuromorphic electronic systems,inspired by the brain’s distinctive information processing mechanisms,have attracted wide attention as a cutting-edge research field by using devices to emulate biologi... In recent years,neuromorphic electronic systems,inspired by the brain’s distinctive information processing mechanisms,have attracted wide attention as a cutting-edge research field by using devices to emulate biological synapses and neurons.However,to integrate seamlessly with biological tissue,particularly human skin,such computing hardware must evolve from conventional rigid designs into flexible,deformable systems that can attach conformally.In this context,flexible neuromorphic electronics has arisen from the convergence of neuromorphic computing and flexible electronics.By emulating synaptic and neuronal functions on flexible substrates,these systems enable brain-inspired information processing with high efficiency and ultralow power consumption,making them attractive for smart wearables,digital health,and brain-computer interface.However,despite rapid advances in flexible synaptic devices and their system-level practical applications,a cross-disciplinary synthesis that connects the materials and device physics to circuit integration and application is still missing.Here,we provide a systematic review following a device-to-system application framework.It first summarizes recent advances in flexible artificial synapses,highlighting their bio-inspired emulation of neural functions.Then,the discussion shifts to neuromorphic circuits and systems constructed from such devices,focusing on collaborative sensing-computing and heterogeneous integration strategies that are advancing toward genuinely integrated sensing-memory-computing systems.We also highlight the emerging applications toward next-generation bio-intelligent systems,including wearables,health monitoring,and human-machine interaction.Finally,the key challenges and future directions are summarized,aiming to provide a valuable reference for developing the next-generation of efficient,intelligent,and biocompatible intelligent bionic hardware. 展开更多
关键词 flexible neuromorphic electronics sensing-memory-computing integration memristor transistor spike-encoding circuits
暂未订购 下载PDF
In-sensor reservoir computing for biometric identification based on MoTe2/BaTiO3optical synapses 认领 引用 被引量：3
2
作者 Zhenqiang Guo Gongjie Liu +5 位作者 Weifeng Zhang Xinhao Li Zhen Zhao Qiuhong Li Haoqi Liu Xiaobing Yan 《InfoMat》 SCIE CSCD 2025年第8期81-96,共16页
The artificial intelligence era has witnessed a surge of demand in detection and recognition of biometric information,with applications from financial services to information security.However,the physical separation o... The artificial intelligence era has witnessed a surge of demand in detection and recognition of biometric information,with applications from financial services to information security.However,the physical separation of sensing,memory,and computational units in traditional biometric systems introduces severe decision latency and operational power consumption.Herein,an in-sensor reservoir computing(RC)system based on MoTe2/BaTiO3optical synapses is proposed to detect and recognize the faces and fingerprints information.In optical operation mode,the device exhibits low energy consumption of 41.2 pJ,long retention time of 3×104s,high endurance of 104switching cycles,and multifunctional sensing-memory-computing visual simulations.The light intensity-dependent optical sensing and multilevel optical storage properties are exploited to achieve sunburned eye simulation and image memory functions.These nonlinear,multi-state,short-term storage,and long-term memory characteristics make MoTe2/BaTiO3optical synapses a suitable reservoir layer and readout layer,with short-term properties to project complicated input features into high-dimensional output features,and long-term properties to be used as a readout layer,thus further building an in-sensor RC system for face and fingerprint recognition.Under the 40%Gaussian noise environment,the system achieves 91.73%recognition accuracy for face and 97.50%for fingerprint images,and experimental verification is carried out,which shows potential in practical applications.These results provide a strategy for constructing a high-performance in-sensor RC system for high-accuracy biometric identification. 展开更多
关键词 2D/ferroelectric heterostructure artificial vision system in-sensor reservoir computing optical synapse sensing-memory-computing
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈