Nanowire design by deep learning for energy efficient photonic technologies

TL;DR

This paper proposes a deep learning framework for designing semiconductor nanowires to create energy-efficient photonic devices, integrating large-scale simulations with machine learning to accelerate innovation.

Contribution

It introduces a novel approach combining multi-million-atom simulations with supervised machine learning for nanowire design, advancing photonic technology development.

Findings

Framework enables rapid nanowire design

Potential for energy-efficient photonic devices

Integration of simulations with machine learning

Abstract

This work describes our vision and proposal for the design of next generation photonic devices based on custom-designed semiconductor nanowires. The integration of multi-million-atom electronic structure and optical simulations with the supervised machine learning models will pave the way for transformative nanowire-based technologies, offering opportunities for the next generation energy-efficient greener photonics.