Prospects and applications of photonic neural networks

Chaoran Huang, Volker J. Sorger, Mario Miscuglio, Mohammed Al-Qadasi, Avilash Mukherjee, Lutz Lampe, Mitchell Nichols, Alexander N. Tait, Thomas Ferreira de Lima, Bicky A. Marquez, Jiahui Wang, Lukas Chrostowski, Mable P. Fok, Daniel Brunner, Shanhui Fan, Sudip Shekhar, Paul R. Prucnal, Bhavin J. Shastri

Research output: Contribution to journalReview articlepeer-review

81 Scopus citations


Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks.

Original languageEnglish (US)
Article number1981155
JournalAdvances in Physics: X
Issue number1
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


  • Photonic neural networks
  • artificial intelligence
  • machine learning
  • neuromorphic computing
  • neuromorphic photonics
  • silicon photonics


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