Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning

Abstract

Physical systems exhibiting neuromechanical functions promise to enable structures with directly encoded autonomy and intelligence. A neuromorphic metamaterials class embodying bioinspired mechanosensing, memory, and learning functionalities obtained by leveraging mechanical instabilities integrated with memristive materials is reported. The prototype system comprises a multistable metamaterial whose bistable dome‐shaped units collectively filter, amplify, and transduce external mechanical inputs over large areas into simple electrical signals using embedded piezoresistive sensors. Dome deformations in nonvolatile memristors triggered by the transduced signals, providing a means to store loading events in measurable material states are recorded. Sequentially applied mechanical inputs result in accumulated memristance changes that allow us to physically encode a Hopfield network into the neuromorphic metamaterials. This physical network learns the history of spatially distributed input patterns. Crucially, the neuromorphic metamaterials can retrieve the learned patterns from the memristors’ final accumulated state. Therefore, the system exhibits the ability to learn without supervised training and retain spatially distributed inputs with minimal external overhead. The system's embodied mechanosensing, memory, and learning capabilities establish an avenue for synthetic neuromorphic metamaterials that learn via tactile interactions. This capability suggests new types of large‐area smart surfaces for robotics, autonomous systems, wearables, and morphing structures subjected to spatiotemporal mechanical loading.

All‐Optical Data Processing with Photon‐Avalanching Nanocrystalline Photonic Synapse

“…Although a very simple, singular operational PAS unit has been studied here, further developments toward designing more complex, hybrid, and parallel optical processors seem feasible, as light confinement and guiding by optical fibers, waveguides, [59] nanoresonators, [13,26,60] metamaterials, [61] or plasmonic nanoantenna [62,63] materials has been intensively studied and may enable input data delivery, reprogramming, input/output data transfer, and storage. A concern related to PAS could be the stimulation source, but robust, cheap, and fast, electronically controlled single mode semiconductor lasers in the NIR spectral region exist (e.g., at 1064 or 852 nm).…”

AI‐Based Metamaterial Design for Wearables

“…[90] Hopfield network encoded neuromorphic metamaterials have been used to develop a system with mechanosensing and simultaneous learning abilities, further increasing the capabilities of tactile sensors. [91] Although arbitrary force range sensing, [88] increased compliance and conformity to curved surfaces, [89] bionic-stress matching and imperfection insensitivity [90] were achieved using conventional design strategies, AI-based design optimization has not been explored thoroughly for wearable tactile sensors despite attracting attention in other fields. For example, to enable faster multi-dimensional problem analysis, a hybrid ML and FEM-based flexible metamaterial design process was developed to construct operational insect wings.…”

Design of Bistable Soft Deployable Structures via a Kirigami‐Inspired Planar Fabrication Approach

“…[2] Recently, bistable mechanisms--possessing two stable equilibrium stateshave been introduced into soft robotics for achieving highperformance and multi-functionalities. [3] They have demonstrated various innovative applications in fast grasping, [4][5][6] shape DOI: 10.1002/admt.202300088 reconfiguration, [7,8] information storage, [9,10] high-speed locomotion on ground [11][12][13] and water, [14,15] adaptive sensing, [16] and mechanical oscillation, [17,18] computation, [19,20] and feedback controls [21] toward achieving autonomy for fully soft machines. Fabrication is non-trivial for fully soft bistable mechanisms, including both pre-shaped and pre-buckled ones.…”