TR2023-004

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

    •  Kojima, K., Koike-Akino, T., Wang, Y., Jung Minwoo, , Brand, M., "Inverse design of two-dimensional freeform metagrating using an adversarial conditional variational autoencoder", SPIE Photonics West, DOI: 10.1117/​12.2650299, January 2023.
      BibTeX TR2023-004 PDF
      • @inproceedings{Kojima2023jan,
      • author = {Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Jung Minwoo and Brand, Matthew},
      • title = {{Inverse design of two-dimensional freeform metagrating using an adversarial conditional variational autoencoder}},
      • booktitle = {Proc. SPIE 12431, Photonic and Phononic Properties of Engineered Nanostructures XIII},
      • year = 2023,
      • month = jan,
      • publisher = {SPIE},
      • doi = {10.1117/12.2650299},
      • url = {https://www.merl.com/publications/TR2023-004}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Electronic and Photonic Devices, Machine Learning

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.