Reconfigurable diffractive optical networks for multiplexed all-optical permutation operations

Guangdong Ma; Xilin Yang; Bijie Bai; Jingxi Li; Yuhang Li; Tianyi Gan; Che-Yung Shen; Yijie Zhang; Yuzhu Li; Mona Jarrahi; Aydogan Ozcan

doi:10.1117/12.3028458

4 October 2024 Reconfigurable diffractive optical networks for multiplexed all-optical permutation operations

Guangdong Ma, Xilin Yang, Bijie Bai, Jingxi Li, Yuhang Li, Tianyi Gan, Che-Yung Shen, Yijie Zhang, Yuzhu Li, Mona Jarrahi, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC1311813 (2024) https://doi.org/10.1117/12.3028458
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

We demonstrate a reconfigurable diffractive deep neural network (termed R‑D2NN) with a single physical model performing a large set of unique permutation operations between an input and output field-of-view by rotating different layers within the diffractive network. Our study numerically demonstrated the efficacy of R‑D2NN by accurately approximating 256 distinct permutation matrices using 4 rotatable diffractive layers. We experimentally validated the proof-of-concept of reconfigurable diffractive networks using terahertz radiation and 3D-printed diffractive layers, achieving high concordance with numerical simulations. The reconfigurable design of R‑D2NN provides scalability with high computing speed and efficient use of materials within a single fabricated model.

Conference Presentation

Citation Download Citation

Guangdong Ma, Xilin Yang, Bijie Bai, Jingxi Li, Yuhang Li, Tianyi Gan, Che-Yung Shen, Yijie Zhang, Yuzhu Li, Mona Jarrahi, and Aydogan Ozcan "Reconfigurable diffractive optical networks for multiplexed all-optical permutation operations", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC1311813 (4 October 2024); https://doi.org/10.1117/12.3028458

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