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Nonlinear encoding in diffractive information processing using linear optical materials.

Yuhang Li1,2,3, Jingxi Li1,2,3, Aydogan Ozcan4,5,6

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

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Summary
This summary is machine-generated.

Nonlinear optical encoding strategies in diffractive processors were compared. Phase encoding offers simpler implementation with accuracy comparable to data repetition methods for specific tasks.

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Area of Science:

  • Optical information processing
  • Computational imaging
  • Artificial intelligence hardware

Background:

  • Diffractive optical processors (DOPs) offer potential for efficient optical information processing.
  • Nonlinear encoding strategies are crucial for enhancing the capabilities of DOPs, especially when compared to digital deep neural networks.
  • Understanding the trade-offs between different nonlinear encoding methods in linear material-based DOPs is essential for advancing optical computing.

Purpose of the Study:

  • To analyze and compare the performance of various nonlinear information encoding strategies in diffractive optical processors.
  • To evaluate the utility and performance gaps of these strategies against state-of-the-art digital deep neural networks.
  • To elucidate the impact of data repetition on the universal linear transformation capability of DOPs.

Main Methods:

  • Comparative analysis of nonlinear encoding strategies, including phase encoding and data repetition-based methods.
  • Evaluation using diverse datasets to assess statistical inference performance.
  • Investigation of diffractive volumes, optical cavities, and cascaded data introduction for data repetition.

Main Results:

  • Data repetition in diffractive volumes diminishes the universal linear transformation capability of DOPs, precluding optical analogs to fully connected or convolutional layers.
  • Despite limitations, data repetition-based diffractive blocks can be trained for specific inference tasks, achieving enhanced accuracy through nonlinear encoding.
  • Phase encoding, without data repetition, presents a simpler nonlinear encoding strategy with statistical inference accuracy comparable to data repetition methods.

Conclusions:

  • Data repetition is not suitable for creating universal diffractive layers analogous to digital neural networks but can be optimized for specific tasks.
  • Phase encoding provides a viable and simpler alternative for nonlinear encoding in DOPs, achieving competitive accuracy.
  • Further exploration of the interplay between linear material-based diffractive systems and nonlinear encoding is crucial for developing advanced visual information processors.