Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities

Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities

Photonics inverse design relies on human experts to search for a design topology that satisfies certain optical specifications with their experience and intuitions, which is relatively labor-intensive, slow, and sub-optimal. Machine learning has emerged as a powerful tool to automate this inverse de...

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Bibliographic Details
Main Authors: Li Renjie, Zhang Ceyao, Xie Wentao, Gong Yuanhao, Ding Feilong, Dai Hui, Chen Zihan, Yin Feng, Zhang Zhaoyu
Format: Article
Language:English
Published: De Gruyter 2023-01-01
Series:Nanophotonics
Subjects:
deep learning
inverse design
nanobeams
nanolasers
photonic crystals
reinforcement learning
Online Access:https://doi.org/10.1515/nanoph-2022-0692

Internet

https://doi.org/10.1515/nanoph-2022-0692

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