Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter

TL;DR

This paper introduces a novel CVAE-based deep learning model with adversarial censoring and active learning to design ultra-compact broadband nano-optic power splitters with arbitrary ratios, achieving high performance across a wide spectrum.

Contribution

The paper presents the first application of CVAE and adversarial censoring in photonics device design, enabling the creation of the smallest broadband power splitter with arbitrary ratio.

Findings

Achieved 90% performance across 1250-1800 nm bandwidth.

Designed the smallest broadband power splitter with arbitrary ratio.

Validated the effectiveness of the CVAE model in photonics applications.

Abstract

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.