Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Inference in artificial intelligence with deep optics and photonics

Nature volume 588, pages 39–47 (2020)Cite this article

Abstract

Artificial intelligence tasks across numerous applications require accelerators for fast and low-power execution. Optical computing systems may be able to meet these domain-specific needs but, despite half a century of research, general-purpose optical computing systems have yet to mature into a practical technology. Artificial intelligence inference, however, especially for visual computing applications, may offer opportunities for inference based on optical and photonic systems. In this Perspective, we review recent work on optical computing for artificial intelligence applications and discuss its promise and challenges.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Timeline of artificial intelligence and related optical and photonic implementations.
Fig. 2: Overview of optical wave propagation.
Fig. 3: Illustration of an optical encoder–electronic decoder system.
Fig. 4: Overview of deep optics and photonics applications I.
Fig. 5: Overview of deep optics and photonics applications II.

Similar content being viewed by others

References

  1. LeCun, Y. et al. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems 2 (NIPS 1989) (ed. Touretzky, D. S.) 396–404 (1990).

  2. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012) (eds Pereira, F. et al.) 1097–1105 (2012).

  3. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    ADS  CAS  PubMed  Google Scholar 

  4. Miller, D. A. B. Waves, modes, communications, and optics: a tutorial. Adv. Opt. Photonics 11, 679–825 (2019).

    ADS  Google Scholar 

  5. Brunner, D., Soriano, M. C., Mirasso, C. R. & Fischer, I. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364 (2013).

    ADS  PubMed  PubMed Central  Google Scholar 

  6. Goodman, J. W., Leonberger, F. J., Kung, S.-Y. & Athale, R. A. Optical interconnections for VLSI systems. Proc. IEEE 72, 850–866 (1984). The first paper to provide a substantial analysis and reasons for the use of optics in interconnection (rather than for logic) in digital systems.

    ADS  Google Scholar 

  7. Miller, D. A. B. Rationale and challenges for optical interconnects to electronic chips. Proc. IEEE 88, 728–749 (2000).

    Google Scholar 

  8. Miller, D. A. B. Attojoule optoelectronics for low-energy information processing and communications. J. Lightwave Technol. 35, 346–396 (2017).

    ADS  CAS  Google Scholar 

  9. Miller, D. A. B. Are optical transistors the logical next step? Nat. Photon. 4, 3–5 (2010).

    ADS  CAS  Google Scholar 

  10. Athale, R. & Psaltis, D. Optical computing: past and future. Opt. Photon. News 27, 32–39 (2016).

    Google Scholar 

  11. Goodman, J. W. Introduction to Fourier Optics (Roberts and Co, 2005).

  12. Liutkus, A. et al. Imaging with nature: compressive imaging using a multiply scattering medium. Sci. Rep. 4, 5552 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Saade, A. et al. Random projections through multiple optical scattering: approximating kernels at the speed of light. In 2016 IEEE Intl Conf. Acoustics, Speech and Signal Processing (ICASSP) 6215–6219 (IEEE, 2016).

  14. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018). An optical implementation using multiple optimized layers for all-optical image classification.

    ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

  15. Chang, J., Sitzmann, V., Dun, X., Heidrich, W. & Wetzstein, G. Hybrid optical–electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep. 8, 12324 (2018). An optical implementation of a single CNN layer demonstrated for hybrid optical–electronic image classification.

    ADS  PubMed  PubMed Central  Google Scholar 

  16. Rosenblatt, F. The Perceptron, A Perceiving and Recognizing Automaton Report no. 85-460-1 (Project Para, Cornell Aeronautical Laboratory, 1957).

  17. Hebb, D. O. The Organization of Behavior (Wiley, 1949).

  18. Widrow, B. & Hoff, M. E. Adaptive switching circuits. In 1960 IRE WESCON Convention Record 96–104 (Institute of Radio Engineers, 1960).

  19. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).

    ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

  20. Carpenter, G. A. & Grossberg, S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vis. Graph. Image Process. 37, 54–115 (1987).

    MATH  Google Scholar 

  21. Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982).

    MathSciNet  MATH  Google Scholar 

  22. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    ADS  MATH  Google Scholar 

  23. Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990).

    Google Scholar 

  24. Farhat, N. H., Psaltis, D., Prata, A. & Paek, E. Optical implementation of the Hopfield model. Appl. Opt. 24, 1469–1475 (1985). Optical implementation of content-addressable associative memory based on the Hopfield model for neural networks and on the addition of nonlinear iterative feedback to a vector–matrix multiplier.

    ADS  CAS  PubMed  Google Scholar 

  25. Denz, C. Optical Neural Networks (Springer Science & Business Media, 2013).

  26. Psaltis, D., Brady, D., Gu, X.-G. & Lin, S. Holography in artificial neural networks. Nature 343, 325–330 (1990). Introduction of nonlinear photorefractive crystals for optical computing.

    ADS  CAS  PubMed  Google Scholar 

  27. Li, H.-Y. S., Qiao, Y. & Psaltis, D. Optical network for real-time face recognition. Appl. Opt. 32, 5026–5035 (1993).

    ADS  CAS  PubMed  Google Scholar 

  28. Miller, D. A. B. Self-configuring universal linear optical component. Photon. Res. 1, 1–15 (2013). Proof that arbitrary linear operations such as singular value decompositions can be performed in optics—not just Fourier transforms and convolutions as in early optical computing.

    ADS  Google Scholar 

  29. Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441 (2017). A silicon photonic neural network using meshes of MZIs for vowel recognition.

    ADS  CAS  Google Scholar 

  30. Fang, M. Y.-S., Manipatruni, S., Wierzynski, C., Khosrowshahi, A. & DeWeese, M. R. Design of optical neural networks with component imprecisions. Opt. Express 27, 14009–14029 (2019).

    ADS  CAS  PubMed  Google Scholar 

  31. Wilkes, C. M. et al. 60 dB high-extinction auto-configured Mach–Zehnder interferometer. Opt. Lett. 41, 5318–5321 (2016).

    ADS  CAS  PubMed  Google Scholar 

  32. 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).

    ADS  Google Scholar 

  33. Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019). A photonic circuit that exploits wavelength division multiplexing techniques for pattern recognition directly in the optical domain.

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tait, A. N. et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep. 7, 7430 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  35. Huang, C. et al. Giant enhancement in signal contrast using integrated all-optical nonlinear thresholder. In 2019 Optical Fiber Communications Conference and Exhibition (OFC) 415–417 (IEEE, 2019).

  36. Nahmias, M. A., Shastri, B. J., Tait, A. N. & Prucnal, P. R. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing. IEEE J. Sel. Top. Quantum Electron. 19, 1800212 (2013).

    Google Scholar 

  37. Amin, R. et al. ITO-based electro-absorption modulator for photonic neural activation function. APL Mater. 7, 081112 (2019).

    ADS  Google Scholar 

  38. Williamson, I. A. D. et al. Reprogrammable electro-optic nonlinear activation functions for optical neural networks. IEEE J. Sel. Top. Quantum Electron. 26, 7700412 (2020).

    CAS  Google Scholar 

  39. Miller, D. A. B. Novel analog self-electrooptic-effect devices. IEEE J. Quantum Electron. 29, 678–698 (1993).

    ADS  Google Scholar 

  40. Srinivasan, S. A. et al. High absorption contrast quantum confined stark effect in ultra-thin Ge/SiGe quantum well stacks grown on Si. IEEE J. Quantum Electron. 56, 5200207 (2020).

    Google Scholar 

  41. Ferreira de Lima, T., Shastri, B. J., Tait, A. N., Nahmias, M. A. & Prucnal, P. R. Progress in neuromorphic photonics. Nanophotonics 6, 577–599 (2017).

    Google Scholar 

  42. Nahmias, M. A. et al. Photonic multiply–accumulate operations for neural networks. IEEE J. Sel. Top. Quantum Electron. 26, 7701518 (2020). A review article on the state-of-the-art of photonic MACs along with detailed characterizations and comparisons of the performance of photonic and comparable electronic hardware.

    CAS  Google Scholar 

  43. Gupta, S., Agrawal, A., Gopalakrishnan, K. & Narayanan, P. Deep learning with limited numerical precision. In Proc. 32nd Intl Conf. Machine Learning (eds Bach, F. & Blei, D.) 1737–1746 (PMLR, 2015).

  44. Hamerly, R., Bernstein, L., Sludds, A., Soljačić, M. & Englund, D. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X 9, 021032 (2019).

    CAS  Google Scholar 

  45. Lugt, A. V. Signal detection by complex spatial filtering. IEEE Trans. Inf. Theory 10, 139–145 (1964). The introduction of optical correlators.

    MATH  Google Scholar 

  46. Gregory, D. A. Real-time pattern recognition using a modified liquid crystal television in a coherent optical correlator. Appl. Opt. 25, 467–469 (1986).

    ADS  CAS  PubMed  Google Scholar 

  47. Manzur, T., Zeller, J. & Serati, S. Optical-correlator-based target detection, recognition, classification, and tracking. Appl. Opt. 51, 4976–4983 (2012).

    ADS  PubMed  Google Scholar 

  48. Javidi, B., Li, J. & Tang, Q. Optical implementation of neural networks for face recognition by the use of nonlinear joint transform correlators. Appl. Opt. 34, 3950–3962 (1995).

    ADS  CAS  PubMed  Google Scholar 

  49. Koppal, S. J., Gkioulekas, I., Zickler, T. & Barrows, G. L. Wide-angle micro sensors for vision on a tight budget. In 2011 IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2011) 361–368 (IEEE, 2011).

  50. Hughes, T. W., Williamson, I. A. D., Minkov, M. & Fan, S. Wave physics as an analog recurrent neural network. Sci. Adv. 5, eaay6946 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  51. Duarte, M. F. et al. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag. 25, 83–91 (2008).

    ADS  Google Scholar 

  52. Moretti, C. & Gigan, S. Readout of fluorescence functional signals through highly scattering tissue. Nat. Photonics 14, 361–364 (2020).

    ADS  CAS  Google Scholar 

  53. Rahmani, B., Loterie, D., Konstantinou, G., Psaltis, D. & Moser, C. Multimode optical fiber transmission with a deep learning network. Light Sci. Appl. 7, 69 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  54. Caramazza, P., Moran, O., Murray-Smith, R. & Faccio, D. Transmission of natural scene images through a multimode fibre. Nat. Commun. 10, 2029 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  55. Li, Y., Xue, Y. & Tian, L. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica 5, 1181–1190 (2018).

    ADS  Google Scholar 

  56. Horisaki, R., Takagi, R. & Tanida, J. Learning-based imaging through scattering media. Opt. Express 24, 13738–13743 (2016).

    ADS  PubMed  Google Scholar 

  57. Ando, T., Horisaki, R. & Tanida, J. Speckle-learning-based object recognition through scattering media. Opt. Express 23, 33902–33910 (2015).

    ADS  CAS  PubMed  Google Scholar 

  58. Mahoney, M. W. Randomized Algorithms for Matrices and Data (Now Publishers, 2011).

  59. Dong, J., Rafayelyan, M., Krzakala, F. & Gigan, S. Optical reservoir computing using multiple light scattering for chaotic systems prediction. IEEE J. Sel. Top. Quantum Electron. 26, 7701012 (2019).

    Google Scholar 

  60. Gupta, S., Gribonval, R., Daudet, L. & Dokmanić, I. Don’t take it lightly: phasing optical random projections with unknown operators. In Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (eds Wallach, H. et al.) 14855–14865 (2019).

  61. Marshall, J. & Oberwinkler, J. The colourful world of the mantis shrimp. Nature 401, 873–874 (1999).

    ADS  CAS  PubMed  Google Scholar 

  62. Thoen, H. T., How, M. J., Chiou, T.-H. & Marshall, J. A different form of color vision in mantis shrimp. Science 343, 411–413 (2014).

    ADS  CAS  PubMed  Google Scholar 

  63. Wetzstein, G., Ihrke, I., Lanman, D. & Heidrich, W. Computational plenoptic imaging. Comput. Graph. Forum 30, 2397–2426 (2011).

    Google Scholar 

  64. Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006).

    ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

  65. Sitzmann, V. et al. End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graph. 37, 114 (2018). The first demonstration of end-to-end optimization of optics and image processing for a computational camera design with computer vision applications.

    Google Scholar 

  66. Chakrabarti, A. Learning sensor multiplexing design through back-propagation. In Advances in Neural Information Processing Systems 29 (NIPS 2016) (eds Lee, D. D. et al.) 3081–3089 (2016).

  67. Martel, J. N. P., Muller, L. K., Carey, S., Dudek, P. & Wetzstein, G. Neural sensors: learning pixel exposures for HDR imaging and video compressive sensing with programmable sensors. IEEE Trans. Pattern Anal. Mach. Intell. 42, 1642–1653 (2020).

    PubMed  Google Scholar 

  68. Horstmeyer, R., Chen, R. Y., Kappes, B. & Judkewitz, B. Convolutional neural networks that teach microscopes how to image. Preprint at https://arxiv.org/abs/1709.07223 (2017).

  69. Marco, J. et al. DeepToF: off-the-shelf real-time correction of multipath interference in time-of-flight imaging. ACM Trans. Graph. 36, 219 (2017).

    Google Scholar 

  70. Su, S., Heide, F., Wetzstein, G. & Heidrich, W. Deep end-to-end time-of-flight imaging. In 2018 IEEE Conf. Computer Vision and Pattern Recognition (CVPR) 6383–6392 (IEEE, 2018).

  71. Kellman, M., Bostan, E., Repina, N. & Waller, L. Physics-based learned design: optimized coded-illumination for quantitative phase imaging. IEEE Trans. Comput. Imaging 5, 344–353 (2019).

    Google Scholar 

  72. Sinha, A., Lee, J., Li, S. & Barbastathis, G. Lensless computational imaging through deep learning. Optica 4, 1117–1125 (2017).

    ADS  Google Scholar 

  73. Metzler, C. A., Ikoma, H., Peng, Y. & Wetzstein, G. Deep optics for single-shot high-dynamic-range imaging. In 2020 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR) 1372–1382 (IEEE, 2020).

  74. Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl. 8, 112 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  75. Haim, H., Elmalem, S., Giryes, R., Bronstein, A. M. & Marom, E. Depth estimation from a single image using deep learned phase coded mask. IEEE Trans. Comput. Imaging 4, 298–310 (2018).

    Google Scholar 

  76. Chang, J. & Wetzstein, G. Deep optics for monocular depth estimation and 3D object detection. In 2019 IEEE/CVF Intl Conf. Computer Vision (ICCV) 10192–10211 (IEEE, 2019).

  77. Wu, Y., Boominathan, V., Chen, H., Sankaranarayanan, A. & Veeraraghavan, A. Phasecam3D—learning phase masks for passive single view depth estimation. In 2019 IEEE Intl Conf. Computational Photography (ICCP) 19–30 (IEEE, 2019).

  78. Bertero, M. & Boccacci, P. Introduction to Inverse Problems in Imaging (CRC Press, 1998).

  79. Barbastathis, G., Ozcan, A. & Situ, G. On the use of deep learning for computational imaging. Optica 6, 921–943 (2019).

    ADS  Google Scholar 

  80. Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437–1443 (2017).

    ADS  Google Scholar 

  81. Wu, Y. et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica 5, 704–710 (2018).

    ADS  Google Scholar 

  82. Nehme, E. & Weiss, L. E., Michaeli, T. & Shechtman, Y. Deep-storm: super-resolution single-molecule microscopy by deep learning. Optica 5, 458–464 (2018).

    ADS  CAS  Google Scholar 

  83. Ouyang, W., Aristov, A., Lelek, M., Hao, X. & Zimmer, C. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol. 36, 460–468 (2018).

    CAS  PubMed  Google Scholar 

  84. Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792–803 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Wu, Y. et al. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning. Nat. Methods 16, 1323–1331 (2019).

    CAS  PubMed  Google Scholar 

  86. Wang, H. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103–110 (2019).

    CAS  PubMed  Google Scholar 

  87. Rivenson, Y., Zhang, Y., Günaydın, H., Teng, D. & Ozcan, A. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl. 7, 17141 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Boyd, N., Jonas, E., Babcock, H. & Recht, B. DeepLoco: Fast 3D localization microscopy using neural networks. Preprint at https://doi.org/10.1101/267096 (2018).

  89. Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15, 1090 (2018).

    CAS  Google Scholar 

  90. Nehme, E. et al. DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning. Nat. Methods 17, 734–740 (2020). An end-to-end optimization approach for point spread function engineering and neural-network-based locations for 3D fluorescence superresolution microscopy.

    CAS  PubMed  Google Scholar 

  91. Liu, T. et al. Deep learning-based super-resolution in coherent imaging systems. Sci. Rep. 9, 3926 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  92. Zhang, H. et al. High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network. Biomed. Opt. Express 10, 1044–1063 (2019).

    PubMed  PubMed Central  Google Scholar 

  93. Escudero, M. C. et al. Digitally stained confocal microscopy through deep learning. In Proc. 2nd Intl Conf. Medical Imaging with Deep Learning (eds Cardoso, M. J. et al.) 121–129 (PMLR, 2019).

  94. Rivenson, Y. et al. Deep learning enhanced mobile-phone microscopy. ACS Photonics 5, 2354–2364 (2018).

    CAS  Google Scholar 

  95. Goy, A., Arthur, K., Li, S. & Barbastathis, G. Low photon count phase retrieval using deep learning. Phys. Rev. Lett. 121, 243902 (2018).

    ADS  CAS  PubMed  Google Scholar 

  96. Rivenson, Y. et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng. 3, 466–477 (2019).

    CAS  PubMed  Google Scholar 

  97. Wu, Y. et al. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light Sci. Appl. 8, 25 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  98. Rivenson, Y. et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl. 8, 23 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  99. Mengu, D., Luo, Y., Rivenson, Y. & Ozcan, A. Analysis of diffractive optical neural networks and their integration with electronic neural networks. IEEE J. Sel. Top. Quantum Electron. 26, 3700114 (2019).

    PubMed  Google Scholar 

  100. Dagenais, M., Sharfin, W. F. & Seymour, R. J. Optical digital matrix multiplication apparatus. EU patent EP0330710A1 (1988).

Download references

Acknowledgements

We thank E. Otte for help designing figures. G.W. was supported by an NSF CAREER Award (IIS 1553333), a Sloan Fellowship, by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant, and a PECASE by the US Army Research Office. A.O. was supported by an NSF ERC (PATHS-UP) grant. S.G. acknowledges funding from the European Research Council (ERC; H2020, SMARTIES-724473) and support from the Institut Universitaire de France. S.F. was supported by the US Air Force Office of Scientific Research (AFOSR) through the MURI project (grant no. FA9550-17-1-0002). D.E. and M.S. were in part supported by the US Army Research Office through the Institute for Soldier Nanotechnologies (grant no. W911NF-18-2-0048). D.E. also acknowledges support from an NSF EAGER programme. D.A.B.M. was supported by the Air Force Office of Scientific Research (award no. FA9550-17-1-0002). P.D. acknowledges discussions and a long-term collaboration with N. Farhat.

Author information

Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Gordon Wetzstein, Shanhui Fan & David A. B. Miller

  2. University of California, Los Angeles, Los Angeles, CA, USA

    Aydogan Ozcan

  3. Laboratoire Kastler Brossel, Sorbonne Université, École Normale Supérieure, Collège de France, CNRS UMR 8552, Paris, France

    Sylvain Gigan

  4. Massachusetts Institute of Technology, Cambridge, MA, USA

    Dirk Englund & Marin Soljačić

  5. University of Münster, Münster, Germany

    Cornelia Denz

  6. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Demetri Psaltis

Authors
  1. Gordon Wetzstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aydogan Ozcan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sylvain Gigan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shanhui Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dirk Englund
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marin Soljačić
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Cornelia Denz
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David A. B. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Demetri Psaltis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.W. conceived the idea, coordinated the writing process, wrote parts of the paper, and edited all sections. A.O., S.G., S.F.., D.E., M.S., C.D., D.A.B.M. and D.P. wrote parts of the paper and provided feedback on all other parts.

Corresponding author

Correspondence to Gordon Wetzstein.

Ethics declarations

Competing interests

M.S. owns stocks of Lightelligence, Inc. S.G. owns stocks of LightOn. D.E. and D.A.B.M. own stocks in Lightmatter Inc. The other authors declare no competing financial interests.

Additional information

Peer review information Nature thanks Geoffrey W. Burr and Nathan Youngblood for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wetzstein, G., Ozcan, A., Gigan, S. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020). https://doi.org/10.1038/s41586-020-2973-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2973-6

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing