Data class-specific all-optical transformations using diffractive computing

Bijie Bai; Heming Wei; Xilin Yang; Tianyi Gan; Deniz Mengu; Mona Jarrahi; Aydogan Ozcan

doi:10.1117/12.2678154

28 September 2023 Data class-specific all-optical transformations using diffractive computing

Bijie Bai, Heming Wei, Xilin Yang, Tianyi Gan, Deniz Mengu, Mona Jarrahi, Aydogan Ozcan

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550U (2023) https://doi.org/10.1117/12.2678154
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

We present data class-specific transformation diffractive networks that all-optically perform different preassigned transformations for different input data classes. The visual information encoded in the amplitude, phase, or intensity channel of the input field is all-optically processed and transformed/encrypted by the diffractive network. The amplitude or intensity of the resulting field approximates the transformed/encrypted input information using the transformation matrix specifically assigned for that data class. We experimentally validated this class-specific transformation framework by designing and fabricating two diffractive networks at 1550nm and 0.75mm wavelengths. The presented framework provides a fast, secure, and energy-efficient solution to data encryption applications.

Conference Presentation

Citation Download Citation

Bijie Bai, Heming Wei, Xilin Yang, Tianyi Gan, Deniz Mengu, Mona Jarrahi, and Aydogan Ozcan "Data class-specific all-optical transformations using diffractive computing", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550U (28 September 2023); https://doi.org/10.1117/12.2678154

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