Authors
Aman Thakkar, Jose R Rivas-Padilla, Andres F Arrieta
Published in
Materials horizons. Jul 29, 2026. Epub Jul 29, 2026.
Abstract
Interest in mechanical computing has surged in recent years, but direct translations from digital to mechanical computation, especially in the context of von Neumann architectures, face scalability challenges. These challenges necessitate alternative strategies beyond miniaturization to enhance information density and facilitate the development of intelligent structures. Neuromorphic architectures that rely on in-memory computing can help overcome these scalability constraints. Additionally, using mechanical properties as a mechanism for information encoding and decoding paves the way to utilize these structures in applications where energy resources are limited or embedded electronics are not feasible. In this paper, we leverage the response of viscoelastic mechanical metamaterial blocks to realize memory augmentation and in-material computation from temporal neural coding. We use an asymmetric bistable viscoelastic mechano-bit abstraction to encode information and a protocol for decoding state information using the global stiffness of the mechanical storage system. We derive fundamental limits on memory capacity for a mechanical memory made from elastic mechano-bits and identify a geometric stiffness design rule to ensure global stiffness uniqueness for each bit elastic memory block combination. The transient memory augmentation is achieved via the combined response of our neuron-inspired viscoelastic mechano-bits (Visco-Bits), showing temporal stiffness modulation that yields augmented mechanical memory storage from the resulting non-abelian, path-dependent mechanical behavior. Subsequently, we examine the information entropy gained by using such viscoelastic mechano-bits and demonstrate that this can exceed the conventional n-bit limit for digital storage. This abstraction of transient memory augmentation can be extended to any mechanical, electrical, or optical system capable of exhibiting temporal modulation in a physical property. We show this via a physical demonstration of a 2-unit viscoelastic memory block and establish the maximum time limit for which the order-dependent memory expansion occurs. Finally, we explore the utility of augmenting the in-memory computational capabilities in viscoelastic metamaterials by temporally encoding two distinct logic gate operations in our Visco-Bit memory block.
PMID:
42525443
Bibliographic data and abstract were imported from PubMed on 29 Jul 2026.
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