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Large-scale nonlinear optical computing with incoherent light via linear diffractive systems

arXiv cs.NEby Alexander Chen, Yuntian Wang, Md Sadman Sakib Rahman, Yuhang Li, Aydogan OzcanApril 1, 20262 min read0 views
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arXiv:2603.29131v1 Announce Type: cross Abstract: Nonlinear computation is essential for various information processing tasks. Optical implementations are attractive because passive light propagation can manipulate high-dimensional signals with extreme throughput and parallelism; yet realizing nonlinear mappings in optical hardware remains challenging due to the weak nonlinearity of optical materials and the large intensities required to induce nonlinear interactions. This challenge is further amplified in many systems that operate with incoherent illumination, motivating a coherence-aware framework for scalable optical nonlinear processing. Here, we show that linear optical systems, in particular, optimized diffractive processors comprising passive surfaces, can perform large-scale nonlin

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Abstract:Nonlinear computation is essential for various information processing tasks. Optical implementations are attractive because passive light propagation can manipulate high-dimensional signals with extreme throughput and parallelism; yet realizing nonlinear mappings in optical hardware remains challenging due to the weak nonlinearity of optical materials and the large intensities required to induce nonlinear interactions. This challenge is further amplified in many systems that operate with incoherent illumination, motivating a coherence-aware framework for scalable optical nonlinear processing. Here, we show that linear optical systems, in particular, optimized diffractive processors comprising passive surfaces, can perform large-scale nonlinear function approximation under spatially incoherent or partially coherent illumination, when preceded by intensity-only input encoding. We quantify how the accuracy of the nonlinear function approximation varies with the degree of parallelism, the number of diffractive layers, and the number of trainable diffractive features. Numerical results demonstrate snapshot computation of up to one million distinct nonlinear functions in a single forward pass through a diffractive processor, with the function outputs spatially multiplexed and read out using densely packed detectors at the output. We further provide a proof-of-concept experimental demonstration under incoherent illumination from a liquid crystal display (LCD), enabled by a model-free in situ learning strategy that jointly optimizes the diffractive profile and detector readout geometry in the presence of hardware imperfections and misalignments. Our findings establish diffractive processors as a massively parallel universal function approximator for both spatially incoherent and partially coherent illumination.

Comments: 33 Pages, 8 Figures

Subjects:

Optics (physics.optics); Neural and Evolutionary Computing (cs.NE); Applied Physics (physics.app-ph)

Cite as: arXiv:2603.29131 [physics.optics]

(or arXiv:2603.29131v1 [physics.optics] for this version)

https://doi.org/10.48550/arXiv.2603.29131

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Aydogan Ozcan [view email] [v1] Tue, 31 Mar 2026 01:27:57 UTC (3,067 KB)

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