All-optical implementations of arbitrary linear transformations using diffractive networks

Onur Kulce; Deniz Mengu; Yair Rivenson; Aydogan Ozcan

doi:10.1117/12.2609325

9 March 2022 All-optical implementations of arbitrary linear transformations using diffractive networks

Onur Kulce, Deniz Mengu, Yair Rivenson, Aydogan Ozcan

Proceedings Volume PC12019, AI and Optical Data Sciences III; PC120190O (2022) https://doi.org/10.1117/12.2609325
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

We present both data-free and data-driven methods for the all-optical synthesis of an arbitrary complex-valued linear transformation using diffractive surfaces. Our analyses reveal that if the total number (N) of spatially-engineered diffractive features/neurons is larger than a threshold, dictated by the multiplication of the number of pixels at the input (I) and output (O) fields-of-views, i.e., N>IxO, both methods succeed in all-optical implementation of the target transformation. However, compared to data-free designs, deep learning-based diffractive designs with multiple diffractive layers are found to achieve significantly larger diffraction efficiencies and their all-optical transformations are much more accurate when N< IxO.

Conference Presentation

Citation Download Citation

Onur Kulce, Deniz Mengu, Yair Rivenson, and Aydogan Ozcan "All-optical implementations of arbitrary linear transformations using diffractive networks", Proc. SPIE PC12019, AI and Optical Data Sciences III, PC120190O (9 March 2022); https://doi.org/10.1117/12.2609325

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