All-Inorganic CsPbBr3 Perovskite Planar-Type Memristors as Optoelectronic Synapses

材料科学 记忆电阻器 钙钛矿(结构) 平面的 光电子学 纳米技术 结晶学 电子工程 计算机图形学(图像) 计算机科学 工程类 化学
作者
Z. Q. Liu,Pengpeng Cheng,Ruyan Kang,Jian Zhou,Xiaoshan Wang,Xian Zhao,Jia Zhao,Zhiyuan Zuo
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:16 (38): 51065-51079 被引量:12
标识
DOI:10.1021/acsami.4c09673
摘要

Mimicking fundamental synaptic working principles with memristors contributes an essential step toward constructing brain-inspired, high-efficiency neuromorphic systems that surpass von Neumann system computers. Here, an electroforming-free planar-type memristor based on a CsPbBr3 single crystal is proposed and exhibits excellent resistive switching (RS) behaviors including stable endurance, ultralow power consumption, and fast switching speed. Furthermore, an optically tunable RS performance is demonstrated by manipulating irradiation intensity and wavelength. Optical analysis techniques such as steady-state photoluminescence and time-resolved photoluminescence are employed to investigate the distribution of Br ions and vacancies before and after quantitative polarization, describing migration dynamic processes to elucidate the RS mechanism. Importantly, a CsPbBr3 single crystal, as the optoelectronic synapse, shows unique potential to emulate photoenhanced synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, spike-timing-dependent plasticity, spike-voltage-dependent plasticity, and learning–forgetting–relearning process with ultralow per synapse event energy consumption. A classical Pavlov’s dog experiment is simulated with a combination of optical and electrical stimulation. Finally, pattern recognition with simulated artificial neural networks based on our synapse reached an accuracy of 93.11%. The special strategy and superior RS characteristics of optoelectronic synapses provide a pathway toward high-performance, energy-efficient neuromorphic electronics.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
银漪发布了新的文献求助10
1秒前
含蓄的灰狼完成签到,获得积分10
1秒前
Kao应助顺其自然_666888采纳,获得10
1秒前
顾矜应助笨笨善若采纳,获得10
2秒前
肥肉叉烧发布了新的文献求助10
2秒前
2秒前
高大人发布了新的文献求助10
2秒前
柚子发布了新的文献求助10
2秒前
Hina完成签到,获得积分10
2秒前
3秒前
mmmmm发布了新的文献求助10
4秒前
4秒前
4秒前
深情安青应助112采纳,获得10
4秒前
Ava应助从嘉采纳,获得10
4秒前
欢喜的之瑶完成签到,获得积分10
4秒前
在水一方应助珝潏采纳,获得10
5秒前
摇瓶子的蜗牛完成签到,获得积分10
5秒前
Kao应助Overtone采纳,获得10
5秒前
可爱的函函应助wang采纳,获得10
5秒前
6秒前
科目三应助苏打采纳,获得10
6秒前
6秒前
酷波er应助cxylalala采纳,获得10
7秒前
美满怀绿完成签到 ,获得积分10
8秒前
8秒前
9秒前
xiao发布了新的文献求助10
9秒前
9秒前
Evina发布了新的文献求助10
9秒前
Lucas应助CYJ采纳,获得10
10秒前
利可君发布了新的文献求助10
10秒前
无聊的听寒完成签到 ,获得积分10
10秒前
10秒前
10秒前
混元灵通完成签到,获得积分10
10秒前
11秒前
河马dd发布了新的文献求助10
11秒前
曹冲发布了新的文献求助10
11秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7239629
求助须知:如何正确求助?哪些是违规求助? 8864763
关于积分的说明 18699224
捐赠科研通 6911052
什么是DOI,文献DOI怎么找? 3195032
关于科研通互助平台的介绍 2367338
邀请新用户注册赠送积分活动 2169594