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

TL;DR

This paper presents a compact, training-free optical processor using Fourier surfaces for parallel image classification, achieving high accuracy and broadband operation in a nanoscale device, enabling scalable and energy-efficient AI computing.

Contribution

The work introduces a novel, passive optical processor based on diffractive Fourier surfaces that operates broadband and supports multiple simultaneous computations without training.

Findings

Achieves up to 84% accuracy on digit datasets.

Supports 20 parallel computations in a compact device.

Operates broadband with wavelength-specific separation.

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.