Universal linear intensity transformations using spatially incoherent diffractive processors

Md Sadman Sakib Rahman; Xilin Yang; Jingxi Li; Bijie Bai; Aydogan Ozcan

doi:10.1038/s41377-023-01234-y

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Universal linear intensity transformations using spatially incoherent diffractive processors

Original Articles

- Universal linear intensity transformations using spatially incoherent diffractive processors
- Light: Science & Applications Vol. 12, Issue 9, Pages: 1830-1856(2023)
- Affiliations：
  
  1.Electrical and 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/s41377-023-01234-y
  CLC：
- Published：30 September 2023，
  
  Published Online：15 August 2023，
  
  Received：22 March 2023，
  
  Revised：14 July 2023，
  
  Accepted：15 July 2023
- Accepted：
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Rahman, M. S. S. et al. Universal linear intensity transformations using spatially incoherent diffractive processors. Light: Science & Applications, 12, 1830-1856 (2023).
DOI：

Rahman, M. S. S. et al. Universal linear intensity transformations using spatially incoherent diffractive processors. Light: Science & Applications, 12, 1830-1856 (2023). DOI： 10.1038/s41377-023-01234-y.

摘要

Abstract

Under spatially coherent light

a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (

) of optimizable phase-only diffractive features is ≥~2

where

and

refer to the number of useful pixels at the input and the output FOVs

respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromat

ic light

the spatially varying intensity point spread function (

) of a diffractive network

corresponding to a given

arbitrarily-selected linear intensity transformation

can be written as

(

;

′

′) = |

(

;

′

′)|

where

is the spatially coherent point spread function of the same diffractive network

and (

) and (

′

′) define the coordinates of the output and input FOVs

respectively. Using numerical simulations and deep learning

supervised through examples of input-output profiles

we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if

≥ ~2

. We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths

operating simultaneously. Finally

we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination

achieving a test accuracy of

95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.

关键词

Keywords

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