Impact of common fabrication errors on the performance of diffractive neural networks

Elena Goi; Mengxiang Chen; Steffen Schoenhardt; Min Gu

doi:10.1117/12.2642249

19 December 2022 Impact of common fabrication errors on the performance of diffractive neural networks

Elena Goi, Mengxiang Chen, Steffen Schoenhardt, Min Gu

Proceedings Volume 12318, Holography, Diffractive Optics, and Applications XII; 123180A (2022) https://doi.org/10.1117/12.2642249
Event: SPIE/COS Photonics Asia, 2022, Online Only

Abstract

We numerically investigate the performance of optical implementations of deep neural network for complex field data processing in the form of multi-layer nanoscale diffractive neural networks, trained to perform image classification tasks. We discuss the parameter optimization and the limitations that fabrication errors put on the performance of such direct phase retrieval systems. The diffractive neural networks studied here may have transformative impact on adaptive optics, data processing and sensing and may be crucial in the development of robust and generalized quantitative phase imaging methods.

Conference Presentation

Citation Download Citation

Elena Goi, Mengxiang Chen, Steffen Schoenhardt, and Min Gu "Impact of common fabrication errors on the performance of diffractive neural networks", Proc. SPIE 12318, Holography, Diffractive Optics, and Applications XII, 123180A (19 December 2022); https://doi.org/10.1117/12.2642249

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