Analogue computing with metamaterials

Abstract

Despite their widespread use for performing advanced computational tasks, digital signal processors suffer from several restrictions, including low speed, high power consumption and complexity, caused by costly analogue-to-digital converters. For this reason, there has recently been a surge of interest in performing wave-based analogue computations that avoid analogue-to-digital conversion and allow massively parallel operation. In particular, novel schemes for wave-based analogue computing have been proposed based on artificially engineered photonic structures, that is, metamaterials. Such kinds of computing systems, referred to as computational metamaterials, can be as fast as the speed of light and as small as its wavelength, yet, impart complex mathematical operations on an incoming wave packet or even provide solutions to integro-differential equations. These much-sought features promise to enable a new generation of ultra-fast, compact and efficient processing and computing hardware based on light-wave propagation. In this Review, we discuss recent advances in the field of computational metamaterials, surveying the state-of-the-art metastructures proposed to perform analogue computation. We further describe some of the most exciting applications suggested for these computing systems, including image processing, edge detection, equation solving and machine learning. Finally, we provide an outlook for the possible directions and the key problems for future research.

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Fig. 1: Wave-based analogue computing based on the Green’s function approach.
Fig. 2: Analogue computing systems based on the Green’s function method.
Fig. 3: Metasurface approach for wave-based analogue computing.
Fig. 4: Acoustic computational metamaterials.
Fig. 5: Wave-based analogue equation solving.
Fig. 6: Robust analogue signal processing.
Fig. 7: Machine learning with metamaterials.

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Acknowledgements

R.F. and F.Z.-N. would like to acknowledge the support from the Swiss National Science Foundation, under grant number 172487. A.A. and D.L.S. acknowledge the support from the Air Force Office of Scientific Research, the Simons Foundation and the National Science Foundation. The authors acknowledge useful discussions with P. del Hougne about machine-learning applications of computing metamaterials.

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Correspondence to Romain Fleury.

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Zangeneh-Nejad, F., Sounas, D.L., Alù, A. et al. Analogue computing with metamaterials. Nat Rev Mater (2020). https://doi.org/10.1038/s41578-020-00243-2

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