Nonlinear encoding in diffractive information processing using linear optical materials

Yuhang Li; Jingxi Li; Aydogan Ozcan

doi:10.1038/s41377-024-01529-8

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Nonlinear encoding in diffractive information processing using linear optical materials

Original Articles

- Nonlinear encoding in diffractive information processing using linear optical materials
- Light: Science & Applications Vol. 13, Issue 8, Pages: 1675-1688(2024)
- Affiliations：
  
  1.Electrical & Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, CA 90095, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-024-01529-8
  CLC：
- Received：02 March 2024，
  
  Revised：11 July 2024，
  
  Accepted：2024-07-12，
  
  Published Online：23 July 2024，
  
  Published：31 August 2024
- Accepted：
Scan QR Code
Li, Y. H., Li, J. X. & Ozcan, A. Nonlinear encoding in diffractive information processing using linear optical materials. Light: Science & Applications, 13, 1675-1688 (2024).
DOI：

Li, Y. H., Li, J. X. & Ozcan, A. Nonlinear encoding in diffractive information processing using linear optical materials. Light: Science & Applications, 13, 1675-1688 (2024). DOI： 10.1038/s41377-024-01529-8.

摘要

Abstract

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here

we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation

we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve

e.g.

phase encoding

against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g.

through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore

data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However

they can still be effectively trained for specific inference tasks and achieve enhanced accuracy

benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

关键词

Keywords

references

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