Machine Learning enables Ultra-Compact Integrated Photonics through Silicon-Nanopattern Digital Metamaterials
Optics
2020-11-30 v2 Machine Learning
Applied Physics
Abstract
In this work, we demonstrate three ultra-compact integrated-photonics devices, which are designed via a machine-learning algorithm coupled with finite-difference time-domain (FDTD) modeling. Through digitizing the design domain into "binary pixels" these digital metamaterials are readily manufacturable as well. By showing a variety of devices (beamsplitters and waveguide bends), we showcase the generality of our approach. With an area footprint smaller than , our designs are amongst the smallest reported to-date. Our method combines machine learning with digital metamaterials to enable ultra-compact, manufacturable devices, which could power a new "Photonics Moore's Law."
Cite
@article{arxiv.2011.11754,
title = {Machine Learning enables Ultra-Compact Integrated Photonics through Silicon-Nanopattern Digital Metamaterials},
author = {Sourangsu Banerji and Apratim Majumder and Alex Hamrick and Rajesh Menon and Berardi Sensale-Rodriguez},
journal= {arXiv preprint arXiv:2011.11754},
year = {2020}
}