All-optical machine learning using diffractive deep neural networks

See allHide authors and affiliations

Science  07 Sep 2018:
Vol. 361, Issue 6406, pp. 1004-1008
DOI: 10.1126/science.aat8084

eLetters is an online forum for ongoing peer review. Submission of eLetters are open to all. eLetters are not edited, proofread, or indexed.  Please read our Terms of Service before submitting your own eLetter.

Compose eLetter

Plain text

  • Plain text
    No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Author Information
First or given name, e.g. 'Peter'.
Your last, or family, name, e.g. 'MacMoody'.
Your email address, e.g.
Your role and/or occupation, e.g. 'Orthopedic Surgeon'.
Your organization or institution (if applicable), e.g. 'Royal Free Hospital'.
Statement of Competing Interests

This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

Vertical Tabs

  • A "diffractive deep neural network" is NOT an all-optical implementation of a conventional deep neural network
    • Haiqing Wei, Scientist, Ambow Research Institute
    • Other Contributors:
      • Gang Huang, Scientist, Ambow Research Institute
      • Xiuqing Wei, Professor, The Third Affiliated Hospital of Sun Yat-sen University
      • Yanlong Sun, Professor, Texas A&M University
      • Hongbin Wang, Professor, Texas A&M University

    Here Lin et al. report a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But readers are strongly advised to draw a clear distinction between the so-called "diffractive deep neural network" (D2NN) and an all-optical implementation of a deep neural network (DNN) in the canonical sense. While the purported D2NN is devoid of any substantially nonlinear signal processing in the middle (hidden) layers, a conventional DNN incorporates nonlinear activations in its middle (hidden) layers and derives powerful computational advantages from them.

    Interested readers are referred to H. Wei et al., "Comment on 'All-optical machine learning using diffractive deep neural networks'," arXiv:1809.08360 ( for detailed discussions, where it has also been rigorously proved that any nonlinearity present or introduced at the output layer of a D2NN or afterward won't be able to boost the pattern discrimination power to beyond the classical Euclidean distance-based algorithms.

    Competing Interests: None declared.

Stay Connected to Science

Navigate This Article