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
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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.
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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。
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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.
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