Inverse design of two-dimensional freeform metagrating using an adversarial conditional variational autoencoder

Keisuke Kojima; Toshiaki Koike-Akino; Ye Wang; Minwoo Jung; Matthew Brand

doi:10.1117/12.2650299

15 March 2023 Inverse design of two-dimensional freeform metagrating using an adversarial conditional variational autoencoder

Keisuke Kojima, Toshiaki Koike-Akino, Ye Wang, Minwoo Jung, Matthew Brand

Author Affiliations +

Proceedings Volume 12431, Photonic and Phononic Properties of Engineered Nanostructures XIII; 124310C (2023) https://doi.org/10.1117/12.2650299
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

For the inverse design of metagratings and metasurfaces, generative deep learning has been widely explored. Most of the works are based on a conditional generative adversarial network (CGAN) and its variants, however, selecting proper hyper parameters for efficient training is challenging. An alternative approach, an adversarial conditional variational autoencoder (A-CVAE) has not been explored yet for the inverse design of metagratings and metasurfaces, even though it has shown great promise for the inverse design of planar nanophotonic waveguide power/wavelength splitters recently. In this paper, we discuss how A-CVAE can be applied for two-dimensional freeform metagratings, including the training dataset preparation, construction of the network, training techniques, and the performance of the inverse-designed metagratings.

Conference Presentation

Citation Download Citation

Keisuke Kojima, Toshiaki Koike-Akino, Ye Wang, Minwoo Jung, and Matthew Brand "Inverse design of two-dimensional freeform metagrating using an adversarial conditional variational autoencoder", Proc. SPIE 12431, Photonic and Phononic Properties of Engineered Nanostructures XIII, 124310C (15 March 2023); https://doi.org/10.1117/12.2650299

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