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Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations

Jingxi Li1,2,3, Yi-Chun Hung1, Onur Kulce1,2,3

  • 1Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.

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Researchers developed a novel polarization-multiplexed diffractive processor for all-optical computing. This device uses deep learning to perform multiple linear transformations simultaneously within a single diffractive network, advancing optical machine vision.

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Area of Science:

  • Optics
  • Machine Learning
  • Optical Computing

Background:

  • Optical computing leverages light for computation, offering potential speed and efficiency advantages.
  • Diffractive optical networks, using engineered surfaces, show promise for all-optical inference and linear transformations.
  • Machine learning advances drive new research in optical computing architectures.

Purpose of the Study:

  • To introduce a polarization-multiplexed diffractive processor for performing multiple, arbitrary linear transformations all-optically.
  • To demonstrate the application of deep learning for training and optimizing such a diffractive network.
  • To explore the capabilities of a single diffractive network for complex optical computations.

Main Methods:

  • Developed a polarization-multiplexed diffractive processor with trainable transmissive diffractive materials and polarizers.
  • Employed deep learning and error-backpropagation for training the network with numerous input/output field examples.
  • Assigned distinct linear transformations to different input/output polarization combinations.

Main Results:

  • A single diffractive network successfully approximated multiple arbitrarily-selected linear transformations with negligible error.
  • Performance improved as the number of trainable diffractive features approached the product of input/output pixels and transformation combinations.
  • The processor demonstrated effective all-optical implementation of complex-valued linear transformations.

Conclusions:

  • The polarization-multiplexed diffractive processor enables efficient all-optical execution of multiple linear transformations.
  • This approach integrates deep learning with passive optical components for advanced optical computing.
  • Potential applications include optical computing and polarization-based machine vision tasks.