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

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 structures, known as Fourier surfaces, for parallel image classification. Our device achieves all-optical classification accuracies of up to 76% for digits and 59% for fashion items within a 40 × 40 μm² footprint. A linear matrix operation on the optical outputs boosts the accuracies to 84% and 66%. 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 6 simultaneous computations. Theoretical inverse-designed extensions show that all-optical classification accuracies up to 94% are achievable within the same physical framework, and on-chip photonic integration could further enhance performance by enabling nonlinear transformations.