Arbitrary control of multimode wave propagation for machine learning

Machine Learning


  • LeCun, Y., Bengio, Y., Hinton, G. Deep Learning. nature 521436–444 (2015).

    Article ADS Google Scholar

  • Patterson, D. et al. Carbon emissions and large-scale neural network training. Preprint at https://arxiv.org/abs/2104.10350 (2021).

  • Shen, Y. et al. Deep learning with coherent nanophotonics circuits. nut. photon. 11441–446 (2017).

    Article ADS Google Scholar

  • Tait, AN et al. Neuromorphic photonic networks using silicon photonic weight banks. Science. Member of Parliament 77430 (2017).

    Article ADS Google Scholar

  • Feldmann, J., Youngblood, N., Wright, CD, Bhaskaran, H. & Pernice, WH All-optical spiking neural synaptic networks with self-learning capabilities. nature 569208–214 (2019).

    Article ADS Google Scholar

  • Feldman, J. et al. Parallel convolution processing using integrated photonic tensor cores. nature 58952–58 (2021).

  • Huang, C. et al. Silicon photonic electronic neural network for fiber nonlinearity compensation. nut. electronic. 4837–844 (2021).

  • Ashtiani, F., Geers, AJ & Aflatouni, F. On-chip photonic deep neural networks for image classification. nature 606501-506 (2022).

  • Bandyopadhyay, S. et al. Single-chip photonic deep neural networks with forward-only training. nut. photon. 181335–1343 (2024).

  • Shastri, BJ et al. Photonics for artificial intelligence and neuromorphic computing. nut. photon. 15102–114 (2021).

  • Hamerly, R., Bernstein, L., Sludds, A., Soljačić, M., Englund, D. Large-scale optical neural networks based on photomultiplication. Physics. Rev.X 9021032 (2019).

    Google Scholar

  • Nahmias, MA et al. Photonic multiply-accumulate operations for neural networks. IEEE J. Sel.Top. quantum electron. 261–18 (2019).

    Article Google Scholar

  • Anderson, M.G., Ma, S.-Y., Wang, T., Wright, L.G. & McMahon, P.L. Optical transformers. Preprint available at https://arxiv.org/abs/2302.10360 (2023).

  • McMahon, PL Physics of Optical Computing. nut. Rev. Phys. 5717–734 (2023).

    Article Google Scholar

  • Hughes, TW, Williamson, IAD, Minkov, M. & Fan, S. Wave physics as analog recurrent neural networks. Science. advanced 5eaay6946 (2019).

    Article ADS Google Scholar

  • Coram, E. et al. Nanophotonics media for artificial neural inference. Photonics Research Institute 7823–827 (2019).

    Article Google Scholar

  • Larocque, H. & Englund, D. Universal linear optics with programmable multimode interference. option. express 2938257–38267 (2021).

  • Masato Nakajima, Kazuya Tanaka, Tetsuya Hashimoto Neural Schrödinger equation: Physical law as a deep neural network. IEEE Trans. Learn Neural Networks. system. 332686–2700 (2022).

    Article MathSciNet Google Scholar

  • Nicker, V. et al. Inversely designed low-index contrast structures on silicon photonics platforms for vector matrix multiplication. nut. photon. 18501–508 (2024).

  • Wu, T., Menarini, M., Gao, Z. & Feng, L. Lithography-free reconfigurable integrated photonic processor. nut. photon. 17710–716 (2023).

  • Molesky, S. et al. Inverse design in nanophotonics. nut. photon. 12659–670 (2018).

    Article ADS Google Scholar

  • Psaltis, D., Brady, D., Gu, X.-G. & Lin, S. Holography of artificial neural networks. nature 343325–330 (1990).

    Article ADS Google Scholar

  • Brady, DJ & Psaltis, D. Holographic interconnects in photorefractive waveguides. Application options. 302324–2333 (1991).

    Article ADS Google Scholar

  • Delaney, M. et al. Non-volatile programmable silicon photonics using ultra-low loss Sb2Se3 Phase change material. Science. advanced 7eabg3500 (2021).

    Article ADS Google Scholar

  • Wu, C. et al. Free-form direct writeable and rewritable photonic integrated circuits in phase change thin films. Science. advanced 10eadk1361 (2024).

    Article Google Scholar

  • Delaney, M., Zeimpekis, I., Lawson, D., Hewak, DW & Muskens, OL A new family of ultra-low loss reversible phase change materials for photonic integrated circuits: Sb2S3 and Sb2Se3. Advanced features. meter. 302002447 (2020).

    Article Google Scholar

  • Chiou, PY, Ohta, AT & Wu, MC Massively parallel manipulation of single cells and microparticles using optical imaging. nature 436370–372 (2005).

    Article ADS Google Scholar

  • Wu, MC optoelectronic tweezers. nut. photon. 5322–324 (2011).

    Article ADS Google Scholar

  • Hillenbrand, J., Getty, LA, Clark, MJ & Wheeler, K. Acoustic properties of American English vowels. J. Acoustic. Social morning. 973099–3111 (1995).

    Article ADS Google Scholar

  • LeCun, Y. MNIST handwritten digits database (New York University, accessed January 7, 2025); http://yann.lecun.com/exdb/mnist/

  • Wright, L. G. et al. Deep physical neural networks trained with backpropagation. nature 601549–555 (2022).

  • Wu, B., Zhou, H., Dong, J. & Zhang, X. Programmable integrated photonic coherent matrix: Principles, configurations, and applications. applied physics. pastor 11011309 (2024).

    Article ADS Google Scholar

  • Clements, WR, Humphreys, PC, Metcalf, BJ, Kolthammer, WS, Walmsley, IA Optimal design of a universal multiport interferometer. optica 31460–1465 (2016).

    Article ADS Google Scholar

  • Hamerly, R., Basani, J.R., Sludds, A., Vadlamani, S. K., and Englund, D. Towards the information-theoretic limits of programmable photonics. APL Photonics 10110803 (2025).

  • DAB Mirror Why optical parts need thickness. science 37941–45 (2023).

    Article ADS MathSciNet Google Scholar

  • Li, S. & Hsu, C.W. Thickness constraints for nonlocal wide-field metalenses. light science. application 11338 (2022).

    Article ADS Google Scholar

  • Gu, J. et al. M3icro: A machine learning-enabled compact photonic tensor core based on programmable multi-operand multimode interference. APL Mach. learn. 2016106 (2024).

  • Huang, C. et al. Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits. APL Photonics 5040803 (2020).

    Article ADS Google Scholar

  • Zhang, H. et al. Optical neural chips for implementing complex-valued neural networks. nut. common. 12457 (2021).

    Article ADS Google Scholar

  • Pai, S. et al. We experimentally realized in-situ backpropagation for deep learning in photonic neural networks. science 380398–404 (2023).

  • Wetzstein, G. et al. Reasoning for artificial intelligence using deep optics and photonics. nature 58839–47 (2020).

    Article ADS Google Scholar

  • Mohammadi Estakhri, N., Edwards, B., Engheta, N. Inverse design metastructure for solving equations. science 3631333–1338 (2019).

    Article ADS MathSciNet Google Scholar

  • Roques-Carmes, C. et al. Heuristic recurrent algorithms for photonic Ising machines. nut. common. 11249 (2020).

    Article ADS Google Scholar

  • Hsu, C.W., Zhen, B., Stone, A.D., Joannopoulos, J.D., Soljačić, M. Bounded states of the continuum. nut. Pastor Mater. 11–13 (2016).

  • Price, H. et al. Roadmap for topological photonics. J. Phys. photonics 4032501 (2022).

  • Marcucci, G., Pierangeli, D. & Conti, C. Theory of neuromorphic computing with waves: Machine learning with rogue waves, distributed shocks, and solitons. Physics. Pastor Rhett. 125093901 (2020).

    Article ADS Google Scholar

  • Teğin, U., Yıldırım, M., Oğuz, İ., Moser, C. & Psaltis, D. Scalable optical learning operators. nut. Calculate. Science. 1542–549 (2021).

    Article Google Scholar

  • Ryo Yanagimoto et al. Programmable on-chip nonlinear photonics. nature (2025).

  • Bender, N. et al. Targeted energy delivery deep within scattering media. nut. Physics. 18309–315 (2022).

    Article Google Scholar

  • Frumker, E. & Silberberg, Y. Phase and amplitude pulse shaping with a two-dimensional phase-limited spatial light modulator. J. Opt. Social AM. B twenty four2940–2947 (2007).

    Article ADS Google Scholar

  • Onodera, T. & Stein, M. Data repository for “Arbitrary control of multimode wave propagation for machine learning.” Zenodo https://doi.org/10.5281/zenodo.10775721 (2025).



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