Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102–114 (2021).
McMahon, P. L. The physics of optical computing. Nat. Rev. Phys. 5, 717–734 (2023).
Bente, I. et al. The potential of multidimensional photonic computing. Nat. Rev. Phys. 7, 439–450 (2025).
Poggio, T., Banburski, A. & Liao, Q. Theoretical issues in deep networks. Proc. Natl Acad. Sci. USA 117, 30039–30045 (2020).
Buckley, S. M., Tait, A. N., McCaughan, A. N. & Shastri, B. J. Photonic online learning: a perspective. Nanophotonics 12, 833–845 (2023).
Kudithipudi, D. et al. Neuromorphic computing at scale. Nature 637, 801–812 (2025).
Maslej, N. et al. The AI Index 2024 Annual Report Technical Report (Stanford University, 2024); https://aiindex.stanford.edu/report/.
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
LeCun, Y. & Bengio, Y. in The Handbook of Brain Theory and Neural Networks (ed. Arbib, M. A.) 255–258 (ACM, 1998).
Daniali, M. & Kim, E. Perception over time: temporal dynamics for robust image understanding. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 5656–5665 (IEEE, 2023).
Goldberg, Y. A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345–420 (2016).
Hirschberg, J. & Manning, C. D. Advances in natural language processing. Science 349, 261–266 (2015).
Arisoy, E., Sainath, T. N., Kingsbury, B. & Ramabhadran, B. Deep neural network language models. In Proc. NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT (eds Ramabhadran, B. et al.) 20–28 (Association for Computational Linguistics, 2012).
Ashtiani, F., Geers, A. J. & Aflatouni, F. An on-chip photonic deep neural network for image classification. Nature 606, 501–506 (2022).
Chen, Y. et al. All-analog photoelectronic chip for high-speed vision tasks. Nature 623, 48–57 (2023).
Xu, X. et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589, 44–51 (2021).
Bandyopadhyay, S. et al. Single-chip photonic deep neural network with forward-only training. Nat. Photon. 18, 1335–1343 (2024).
Ahmed, S. R. et al. Universal photonic artificial intelligence acceleration. Nature 640, 368–374 (2025).
Zhou, T., Wu, W., Zhang, J., Yu, S. & Fang, L. Ultrafast dynamic machine vision with spatiotemporal photonic computing. Sci. Adv. 9, eadg4391 (2023).
Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589, 52–58 (2021).
Wang, T. et al. Image sensing with multilayer nonlinear optical neural networks. Nat. Photon. 17, 408–415 (2023).
Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020).
Huang, C. et al. A silicon photonic–electronic neural network for fibre nonlinearity compensation. Nat. Electron. 4, 837–844 (2021).
Miscuglio, M. et al. Massively parallel amplitude-only Fourier neural network. Optica 7, 1812–1819 (2020).
Argyris, A., Bueno, J. & Fischer, I. Photonic machine learning implementation for signal recovery in optical communications. Sci. Rep. 8, 8487 (2018).
Wang, B., De Lima, T. F., Shastri, B. J., Prucnal, P. R. & Huang, C. Multi-wavelength photonic neuromorphic computing for intra and inter-channel distortion compensations in WDM optical communication systems. IEEE J. Sel. Top. Quantum Electron. 29, 7400212 (2022).
Wright, L. G. et al. Deep physical neural networks trained with backpropagation. Nature 601, 549–555 (2022).
Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).
Spall, J., Guo, X. & Lvovsky, A. I. Hybrid training of optical neural networks. Optica 9, 803–811 (2022).
Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441–446 (2017).
Filipovich, M. J. et al. Silicon photonic architecture for training deep neural networks with direct feedback alignment. Optica 9, 1323–1332 (2022).
Tan, J. Y. et al. Monadic Pavlovian associative learning in a backpropagation-free photonic network. Optica 9, 792–802 (2022).
Wu, B. et al. Scaling up for end-to-end on-chip photonic neural network inference. Light Sci. Appl. 14, 328 (2025).
Xue, Z. et al. Fully forward mode training for optical neural networks. Nature 632, 280–286 (2024).
Pai, S. et al. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science 380, 398–404 (2023).
Spall, J., Guo, X. & Lvovsky, A. I. Training neural networks with end-to-end optical backpropagation. Adv. Photonics 7, 016004 (2025).
Menon, A., Mehrotra, K., Mohan, C. K. & Ranka, S. Characterization of a class of sigmoid functions with applications to neural networks. Neural Netw. 9, 819–835 (1996).
Banerjee, C., Mukherjee, T. & Pasiliao Jr, E. An empirical study on generalizations of the ReLU activation function. In Proc. 2019 ACM Southeast Conference 164–167 (Association for Computing Machinery, 2019).
Hughes, T. W., Minkov, M., Shi, Y. & Fan, S. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica 5, 864–871 (2018).
Abadi, M. et al. An end-to-end platform for machine learning. TensorFlow https://www.tensorflow.org/ (2015).
Rakowski, M. et al. 45nm CMOS-silicon photonics monolithic technology (45CLO) for next-generation, low power and high speed optical interconnects. In Proc. Optical Fiber Communication Conference T3H–3 (Optica Publishing Group, 2020).
Omirzakhov, K., Hao, H., Pirmoradi, A. & Aflatouni, F. Energy efficient monolithically integrated 256 Gb/s optical transmitter with autonomous wavelength stabilization in 45 nm CMOS SOI. IEEE J. Solid State Circuits 60, 2522–2531 (2024).
Chan, D. W. U. et al. C-band 67 GHz silicon photonic microring modulator for dispersion-uncompensated 100 Gbaud PAM-4. Opt. Lett. 47, 2935–2938 (2022).
Pantouvaki, M. et al. Active components for 50 Gb/s NRZ-OOK optical interconnects in a silicon photonics platform. J. Lightwave Technol. 35, 631–638 (2017).
Jo, D.-S., Sung, B.-R.-S., Seo, M.-J., Kim, W.-C. & Ryu, S.-T. A 40-nm CMOS 7-b 32 GS/s SAR ADC with background channel mismatch calibration. IEEE Trans. Circuits Syst. II Express Briefs 67, 610–614 (2020).
Chandrakumar, H. et al. A 48-dB SFDR, 43-dB SNDR, 50-GS/s 9-b 2×-interleaved Nyquist DAC in Intel 16. IEEE Solid State Circuits Lett. 5, 239–242 (2022).
Nielsen, M. A. Neural Networks and Deep Learning, Vol. 25 (Determination Press, 2015).
Kingma, D. P. Adam: A method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Ashtiani, F. & Idjadi, M. H. On-chip nonlinear activation and gradient functions for photonic backpropagation training and inference. In Proc. 2023 IEEE Photonics Conference (IPC) 1–2 (IEEE, 2023).
Siew, S. Y. et al. Review of silicon photonics technology and platform development. J. Lightwave Technol. 39, 4374–4389 (2021).

