Mechanical intelligence via fully reconfigurable elastic neuromorphic metasurfaces

IF 5.3 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY APL Materials Pub Date : 2024-05-15 DOI:10.1063/5.0201761
M. Moghaddaszadeh, M. Mousa, A. Aref, M. Nouh
{"title":"Mechanical intelligence via fully reconfigurable elastic neuromorphic metasurfaces","authors":"M. Moghaddaszadeh, M. Mousa, A. Aref, M. Nouh","doi":"10.1063/5.0201761","DOIUrl":null,"url":null,"abstract":"The ability of mechanical systems to perform basic computations has gained traction over recent years, providing an unconventional alternative to digital computing in off grid, low power, and severe environments, which render the majority of electronic components inoperable. However, much of the work in mechanical computing has focused on logic operations via quasi-static prescribed displacements in origami, bistable, and soft deformable matter. Here, we present a first attempt to describe the fundamental framework of an elastic neuromorphic metasurface that performs distinct classification tasks, providing a new set of challenges, given the complex nature of elastic waves with respect to scattering and manipulation. Multiple layers of reconfigurable waveguides are phase-trained via constant weights and trainable activation functions in a manner that enables the resultant wave scattering at the readout location to focus on the correct class within the detection plane. We further demonstrate the neuromorphic system’s reconfigurability in performing two distinct tasks, eliminating the need for costly remanufacturing.","PeriodicalId":7985,"journal":{"name":"APL Materials","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"APL Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1063/5.0201761","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

The ability of mechanical systems to perform basic computations has gained traction over recent years, providing an unconventional alternative to digital computing in off grid, low power, and severe environments, which render the majority of electronic components inoperable. However, much of the work in mechanical computing has focused on logic operations via quasi-static prescribed displacements in origami, bistable, and soft deformable matter. Here, we present a first attempt to describe the fundamental framework of an elastic neuromorphic metasurface that performs distinct classification tasks, providing a new set of challenges, given the complex nature of elastic waves with respect to scattering and manipulation. Multiple layers of reconfigurable waveguides are phase-trained via constant weights and trainable activation functions in a manner that enables the resultant wave scattering at the readout location to focus on the correct class within the detection plane. We further demonstrate the neuromorphic system’s reconfigurability in performing two distinct tasks, eliminating the need for costly remanufacturing.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过完全可重构的弹性神经形态元表面实现机械智能
近年来,机械系统执行基本计算的能力受到越来越多的关注,它为离网、低功耗和恶劣环境下的数字计算提供了一种非常规的替代方案,而这些环境会导致大多数电子元件无法工作。然而,机械计算的大部分工作都集中在通过折纸、双稳态和软可变形物质中的准静态规定位移进行逻辑运算。在此,我们首次尝试描述弹性神经形态元表面的基本框架,它可以执行不同的分类任务,鉴于弹性波在散射和操纵方面的复杂性质,它提供了一系列新的挑战。多层可重构波导通过恒定权重和可训练激活函数进行相位训练,从而使读出位置的波散射结果聚焦于检测平面内的正确类别。我们进一步展示了神经形态系统在执行两项不同任务时的可重构性,从而消除了昂贵的再制造需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
APL Materials
APL Materials NANOSCIENCE & NANOTECHNOLOGYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
9.60
自引率
3.30%
发文量
199
审稿时长
2 months
期刊介绍: APL Materials features original, experimental research on significant topical issues within the field of materials science. In order to highlight research at the forefront of materials science, emphasis is given to the quality and timeliness of the work. The journal considers theory or calculation when the work is particularly timely and relevant to applications. In addition to regular articles, the journal also publishes Special Topics, which report on cutting-edge areas in materials science, such as Perovskite Solar Cells, 2D Materials, and Beyond Lithium Ion Batteries.
期刊最新文献
Energy harvesting and human motion sensing of a 2D piezoelectric hybrid organic–inorganic perovskite A first-principles study on structural stability and magnetoelectric coupling of two-dimensional BaTiO3 ultrathin film with Cr and Cu substituting Ti site Investigation of transverse exchange-springs in electrodeposited nano-heterostructured films through first-order reversal curve analysis Solid phase epitaxy of SrRuO3 encapsulated by SrTiO3 membranes Microgel-based etalon membranes: Characterization and properties
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1