# Broadband, compact, and training-free optical processors for parallel image classification

Source: [Broadband, compact, and training-free optical processors for parallel image classification](<https://www.sota2.com/research/paper/699d30616017c4460ffab209>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-25T05:00:03.907Z

Authors: Sander J. W. Vonk, Boris de Jong, Yannik M. Glauser, David B. Seda, Matthieu F. Bidaut, Benjamin Savinson, Hannah Niese, David J. Norris

Published: 2026-02-21T10:04:14.000Z

## Abstract

As artificial intelligence becomes increasingly prevalent, the demand for faster and more energy-efficient computing approaches grows. While optical computing offers intrinsic advantages in bandwidth and power consumption, existing implementations remain bulky, wavelength-specific, and dependent on complex training procedures, limiting scalability and parallel operation. In this work, we demonstrate a compact, training-free optical processor based on wavy diffractive features, known as Fourier surfaces, for parallel image classification. Our device achieves classification accuracies of up to 84% for digit datasets and 66% for fashion datasets within a 40$\\times$40 $\\mu$m$^2$ footprint. The diffractive layer inherently separates incident wavelengths into distinct output directions, enabling broadband operation and allowing multiple colors to function as independent computation channels. As a result, this passive system supports up to 20 simultaneous computations within a single optical pass. These results highlight the potential of nanoscale diffractive systems to achieve high compute densities, paving the way for scalable, low-power optical processors for machine learning and image-recognition applications.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.18778>)

[PDF](<https://arxiv.org/pdf/2602.18778.pdf>)