Inverse-designed low-index-contrast structures on a silicon photonics platform for vector–matrix multiplication

Cordaro, A. et al. Solving integral equations in free-space with inverse-designed ultrathin optical metagratings. Nat. Nanotechnol. 18, 365–372 (2023). 10.1038/s41565-022-01297-9

[2]

Nikkhah, V., Tzarouchis, D. C., Hoorfar, A. & Engheta, N. Inverse-designed metastructures together with reconfigurable couplers to compute forward scattering. ACS Photonics 10, 977–985 (2022).

[3]

Silva, A. et al. Performing mathematical operations with metamaterials. Science 343, 160–163 (2014). 10.1126/science.1242818

[4]

Mohammadi Estakhri, N., Edwards, B. & Engheta, N. Inverse-designed metastructures that solve equations. Science 363, 1333–1338 (2019). 10.1126/science.aaw2498

[5]

Zhang, H. et al. An optical neural chip for implementing complex-valued neural network. Nat. Commun. 12, 457 (2021). 10.1038/s41467-020-20719-7

[6]

Camacho, M., Edwards, B. & Engheta, N. A single inverse-designed photonic structure that performs parallel computing. Nat. Commun. 12, 1466 (2021). 10.1038/s41467-021-21664-9

[7]

Fu, W. et al. Ultracompact meta-imagers for arbitrary all-optical convolution. Light Sci. Appl. 11, 62 (2022). 10.1038/s41377-022-00752-5

[8]

Pan, D. et al. Laplace metasurfaces for optical analog computing based on quasi-bound states in the continuum. Photon. Res. 9, 1758–1766 (2021). 10.1364/prj.426827

[9]

Zangeneh-Nejad, F., Sounas, D. L., Alù, A. & Fleury, R. Analogue computing with metamaterials. Nat. Rev. Mater. 6, 207–225 (2021). 10.1038/s41578-020-00243-2

[10]

Zhu, T. et al. Plasmonic computing of spatial differentiation. Nat. Commun. 8, 15391 (2017). 10.1038/ncomms15391

[11]

Pors, A., Nielsen, M. G. & Bozhevolnyi, S. I. Analog computing using reflective plasmonic metasurfaces. Nano Lett. 15, 791–797 (2015). 10.1021/nl5047297

[12]

Wang, H. et al. Design of compact meta-crystal slab for general optical convolution. ACS Photonics 9, 1358–1365 (2022). 10.1021/acsphotonics.1c02005

[13]

Choutagunta, K., Roberts, I., Miller, D. A. & Kahn, J. M. Adapting Mach–Zehnder mesh equalizers in direct-detection mode-division-multiplexed links. J. Light. Technol. 38, 723–735 (2019). 10.1109/jlt.2019.2952060

[14]

Knill, E., Laflamme, R. & Milburn, G. J. A scheme for efficient quantum computation with linear optics. Nature 409, 46–52 (2001). 10.1038/35051009

[15]

Nikkhah, V., Mencagli, M. J. & Engheta, N. Reconfigurable nonlinear optical element using tunable couplers and inverse-designed structure. Nanophotonics 12, 3019–3027 (2023). 10.1515/nanoph-2023-0152

[16]

An optical neural network using less than 1 photon per multiplication

Tianyu Wang, Shi-Yuan Ma, Logan G. Wright et al.

Nature Communications 2022 10.1038/s41467-021-27774-8

[17]

Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102–114 (2021). 10.1038/s41566-020-00754-y

[18]

Deep learning with coherent nanophotonic circuits

Yichen Shen, Nicholas C. Harris, Scott Skirlo et al.

Nature Photonics 2017 10.1038/nphoton.2017.93

[19]

Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018). 10.1126/science.aat8084

[20]

Parallel convolutional processing using an integrated photonic tensor core

J. Feldmann, N. Youngblood, M. Karpov et al.

Nature 2021 10.1038/s41586-020-03070-1

[21]

Wang, H., Guo, C., Zhao, Z. & Fan, S. Compact incoherent image differentiation with nanophotonic structures. ACS Photonics 7, 338–343 (2020). 10.1021/acsphotonics.9b01465

[22]

Zhu, T. et al. Topological optical differentiator. Nat. Commun. 12, 680 (2021). 10.1038/s41467-021-20972-4

[23]

Goh, H. & Alù, A. Nonlocal scatterer for compact wave-based analog computing. Phys. Rev. Lett. 128, 073201 (2022). 10.1103/physrevlett.128.073201

[24]

Farhat, N. H. Photonic neural networks and learning machines. IEEE Expert 7, 63–72 (1992). 10.1109/64.163674

[25]

Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020). 10.1038/s41586-020-2973-6

[26]

Miller, D. A. Self-configuring universal linear optical component. Photon. Res. 1, 1–15 (2013). 10.1364/prj.1.000001

[27]

Bogaerts, W. et al. Programmable photonic circuits. Nature 586, 207–216 (2020). 10.1038/s41586-020-2764-0

[28]

Optimal design for universal multiport interferometers

William R. Clements, Peter C. Humphreys, Benjamin J. Metcalf et al.

Optica 2016 10.1364/optica.3.001460

[29]

Tzarouchis, D. C., Mencagli, M. J., Edwards, B. & Engheta, N. Mathematical operations and equation solving with reconfigurable metadevices. Light Sci. Appl. 11, 263 (2022). 10.1038/s41377-022-00950-1

[30]

Bendsoe, M. P. & Sigmund, O. Topology Optimization: Theory, Methods and Applications (Springer, 2004). 10.1007/978-3-662-05086-6_2

[31]

Inverse design in nanophotonics

Sean Molesky, Zin Lin, Alexander Y. Piggott et al.

Nature Photonics 2018 10.1038/s41566-018-0246-9

[32]

Hughes, T. W., Minkov, M., Williamson, I. A. & Fan, S. Adjoint method and inverse design for nonlinear nanophotonic devices. ACS Photonics 5, 4781–4787 (2018). 10.1021/acsphotonics.8b01522

[33]

Hammer, M. & Ivanova, O. V. Effective index approximations of photonic crystal slabs: a 2-to-1-D assessment. Opt. Quantum Electron. 41, 267–283 (2009). 10.1007/s11082-009-9349-3

[34]

Knox, R. & Toulios, P. Integrated circuits for the millimeter through optical frequency range. Proc. Symp. Submillimeter Waves 20, 497–515 (1970).

[35]

Chiang, K. S. Analysis of optical fibers by the effective-index method. Appl. Opt. 25, 348–354 (1986). 10.1364/ao.25.000348

[36]

Hocker, G. B. & Burns, W. K. Mode dispersion in diffused channel waveguides by the effective index method. Appl. Opt. 16, 113–118 (1977). 10.1364/ao.16.000113

[37]

Van De Velde, K., Thienpont, H. & Van Geen, R. Extending the effective index method for arbitrarily shaped inhomogeneous optical waveguides. J. Light. Technol. 6, 1153–1159 (1988). 10.1109/50.4108

[38]

COMSOL Multiphysics v. 5.6 (COMSOL; 2023); www.comsol.com

[39]

Nikkhah, V. et al. Replication data for ‘Inverse-designed low-index-contrast structures on silicon photonics platform for vector-matrix multiplication’ (Zenodo, 2023); https://doi.org/10.5281/zenodo.10083901 10.5281/zenodo.10083901

Cited By

81

Diffractive optical computing in free space

Jingtian Hu, Deniz Mengu · 2024

Nature Communications

Metrics

81

Citations

39

References

Details

Published: Feb 16, 2024
Vol/Issue: 18(5)
Pages: 501-508
License: View

Authors

Nokia Bell Labs 600 Mountain Avenue New Providence NJ 07974 USA

B

Brian Edwards

Assistant Professor, Department of Kinesiology and Health Wright State University, 725 University Blvd, Dayton, OH 45435, Phone: 937.245.7622, Email: brian.edwards@wright.edu

Cite This Article

Vahid Nikkhah, Ali Pirmoradi, Farshid Ashtiani, et al. (2024). Inverse-designed low-index-contrast structures on a silicon photonics platform for vector–matrix multiplication. Nature Photonics, 18(5), 501-508. https://doi.org/10.1038/s41566-024-01394-2

Inverse-designed low-index-contrast structures on a silicon photonics platform for vector–matrix multiplication

You May Also Like