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13 March 2024 Massively parallel all-optical visual computing using a wavelength-multiplexed diffractive optical processor
Jingxi Li, Tianyi Gan, Bijie Bai, Yi Luo, Mona Jarrahi, Aydogan Ozcan
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Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030Z (2024) https://doi.org/10.1117/12.3001971
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Diffractive deep neural networks utilize successive, spatially-engineered diffractive surfaces trained via deep learning to all-optically process input optical fields based on a desired transformation. We present the design of a broadband diffractive network that can all-optically perform a large set of arbitrary complex-valued linear transformations, wherein the input/output data are encoded at W different wavelength channels, each assigned to a unique linear transformation, covering, e.g., W>100-2000. This broadband diffractive visual processor may foster the development of all-optical visual processors with substantial data bandwidth and parallel computation capabilities, creating intelligent machine vision systems for all-optical processing of multi-color or hyperspectral objects/scenes.
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Jingxi Li, Tianyi Gan, Bijie Bai, Yi Luo, Mona Jarrahi, and Aydogan Ozcan "Massively parallel all-optical visual computing using a wavelength-multiplexed diffractive optical processor", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030Z (13 March 2024); https://doi.org/10.1117/12.3001971
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KEYWORDS
Visualization
Parallel computing
Visual optics
Data processing
Design and modelling
Education and training
Error analysis
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