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Broadband, Compact, and Training-Free Optical Processors for Parallel Image Classification.

Sander J W Vonk1, Boris de Jong1, Yannik M Glauser1

  • 1Optical Materials Engineering Laboratory, Department of Mechanical and Process Engineering, ETH Zurich, Zurich 8092, Switzerland.

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|June 19, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a compact, training-free optical processor using Fourier surfaces for faster, energy-efficient AI image classification. The novel device achieves high accuracies and supports multiple simultaneous computations, paving the way for advanced optical computing.

Keywords:
Fourier surfacesimage classificationmachine learningnanofabricationoptical computing

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Area of Science:

  • Photonics
  • Optical Computing
  • Artificial Intelligence

Background:

  • Growing demand for faster, energy-efficient computing in AI.
  • Limitations of current optical computing: bulky, wavelength-specific, complex training.
  • Need for scalable and parallel optical processing solutions.

Purpose of the Study:

  • Demonstrate a compact, training-free optical processor for parallel image classification.
  • Overcome limitations of existing optical computing implementations.
  • Explore broadband operation and multi-channel computation capabilities.

Main Methods:

  • Utilized wavy diffractive structures (Fourier surfaces) for optical processing.
  • Developed a compact device with a 40 × 40 μm² footprint.
  • Implemented all-optical classification and post-processing linear matrix operations.

Main Results:

  • Achieved all-optical classification accuracies of 76% (digits) and 59% (fashion items).
  • Boosted accuracies to 84% (digits) and 66% (fashion items) with linear matrix operations.
  • Demonstrated broadband operation with up to 6 simultaneous computations due to wavelength separation.

Conclusions:

  • The Fourier surface processor offers a compact, efficient, and scalable solution for optical image classification.
  • The passive system's ability to handle multiple wavelengths enables parallel computation.
  • Future work includes inverse-designed extensions and on-chip photonic integration for enhanced performance.