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Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter by Tang, Yingheng; Kojima, Keisuke; Koike-Akino, Toshiaki; Wang, Ye; Wu, Pengxiang; Tahersima, Mohammad; Jha, Devesh K.; Parsons, Kieran; Qi, Minghao is a scholarly article available to read on EtoBox.

What is Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter about?

We propose a novel Conditional Variational Autoencoder (CVAE) model, enhanced with adversarial censoring and active learning, for the generation of 550 nm broad bandwidth (1250 nm to 1800 nm) power splitters with arbitrary splitting ratio. The device footprint is 2.25 x 2.25 {\mu} m2 with a 20 x 20 etched hole combination. It is the first demonstration to apply the CVAE model and the adversarial censoring for the photonics problems. We confirm that the optimized device has an overall performance close to 90% across all bandwidths from 1250 nm to 1800 nm. To the best of our knowledge, this is the smallest broadband power splitter with arbitrary ratio.

Author

Tang, Yingheng; Kojima, Keisuke; Koike-Akino, Toshiaki; Wang, Ye; Wu, Pengxiang; Tahersima, Mohammad; Jha, Devesh K.; Parsons, Kieran; Qi, Minghao

Published

2020

Language

EN