Metadevices that compute: mathematical foundations and experimental implementations for neuromorphic nanophotonics
Abstract
Wave-based analog computing has re-emerged as a compelling alternative to digital electronics for specific linear-algebraic and machine-learning workloads, promising massively parallel, high-bandwidth, and energy-efficient information processing. Metamaterials, metasurfaces, and integrated metastructures (collectively, metadevices) now perform mathematical operations, solve integral and differential equations, multiply matrices, and implement trainable photonic neural networks directly in the wave domain. This focused review consolidates the mathematical foundations that underpin such devices, i.e., the mapping between linear operators and wave scattering, the feedback architectures that realize equation inversion, and the inverse-design and scientific machine-learning toolchains that make their synthesis and training tractable, and connects them to the rapidly growing body of experimental demonstrations, from free-space diffractive processors to reconfigurable guided-wave metastructures. We conclude with open problems and opportunities for this emerging neuromorphic nanophotonics ecosystem.
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BibTeX
@misc{hadia2026metadevices,
title = {{Metadevices that compute: mathematical foundations and experimental implementations for neuromorphic nanophotonics}},
author = {Xian Hadia and Odysseas Tsilipakos and Dimitrios C. Tzarouchis and Raphaël Pestourie},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40903},
eprint = {2610.40903},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40903},
}
RIS
TY - UNPB
TI - Metadevices that compute: mathematical foundations and experimental implementations for neuromorphic nanophotonics
AU - Xian Hadia
AU - Odysseas Tsilipakos
AU - Dimitrios C. Tzarouchis
AU - Raphaël Pestourie
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40903
UR - https://arxiv.org/abs/2610.40903
AB - Wave-based analog computing has re-emerged as a compelling alternative to digital electronics for specific linear-algebraic and machine-learning workloads, promising massively parallel, high-bandwidth, and energy-efficient information processing. Metamaterials, metasurfaces, and integrated metastructures (collectively, metadevices) now perform mathematical operations, solve integral and differential equations, multiply matrices, and implement trainable photonic neural networks directly in the wave domain. This focused review consolidates the mathematical foundations that underpin such devices, i.e., the mapping between linear operators and wave scattering, the feedback architectures that realize equation inversion, and the inverse-design and scientific machine-learning toolchains that make their synthesis and training tractable, and connects them to the rapidly growing body of experimental demonstrations, from free-space diffractive processors to reconfigurable guided-wave metastructures. We conclude with open problems and opportunities for this emerging neuromorphic nanophotonics ecosystem.
ER -