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Generative Deep Learning Model for Inverse Design of Integrated Nanophotonic Devices
Author(s) -
Tang Yingheng,
Kojima Keisuke,
KoikeAkino Toshiaki,
Wang Ye,
Wu Pengxiang,
Xie Youye,
Tahersima Mohammad H.,
Jha Devesh K.,
Parsons Kieran,
Qi Minghao
Publication year - 2020
Publication title -
laser and photonics reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.778
H-Index - 116
eISSN - 1863-8899
pISSN - 1863-8880
DOI - 10.1002/lpor.202000287
Subject(s) - broadband , autoencoder , computer science , bandwidth (computing) , inverse , photonics , splitter , nanophotonics , deep learning , electronic engineering , algorithm , artificial intelligence , materials science , optoelectronics , optics , physics , mathematics , telecommunications , engineering , geometry
Abstract A novel conditional variational autoencoder (CVAE) model for designing nanopatterned integrated photonic components is proposed. In particular, it is shown that prediction capability of the CVAE model can be significantly improved by adversarial censoring and active learning. Moreover, generation of nanopatterned power splitters with arbitrary splitting ratios and 550 nm broadband optical responses from 1250 to 1800 nm are demonstrated. Nanopatterned power splitters with footprints of 2.25 × 2.25 μ m 2 and 20 × 20 etch hole positions are the design space, with each hole position assuming a radius from a range of radii. Designed nanopatterned power splitters using methods presented herein demonstrate an overall transmission of about 90% across the operating bandwidth from 1250 to 1800 nm. To the best of authors' knowledge, this is the first time that a state‐of‐the‐art CVAE deep neural network model is successfully used to design a physical device.