Broadband, compact, and training-free optical processors for parallel image classification
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arXiv
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| Format: | Preprint |
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2026
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| author | Vonk, Sander J. W. de Jong, Boris Glauser, Yannik M. Seda, David B. Bidaut, Matthieu F. Savinson, Benjamin Niese, Hannah Norris, David J. |
| author_facet | Vonk, Sander J. W. de Jong, Boris Glauser, Yannik M. Seda, David B. Bidaut, Matthieu F. Savinson, Benjamin Niese, Hannah Norris, David J. |
| contents | 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 $μ$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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18778 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Broadband, compact, and training-free optical processors for parallel image classification Vonk, Sander J. W. de Jong, Boris Glauser, Yannik M. Seda, David B. Bidaut, Matthieu F. Savinson, Benjamin Niese, Hannah Norris, David J. Optics 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 $μ$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. |
| title | Broadband, compact, and training-free optical processors for parallel image classification |
| topic | Optics |
| url | https://arxiv.org/abs/2602.18778 |