Abstract

In this Letter, we demonstrate how harmonic oscillator equations can be integrated in a neural network to improve the spectral response prediction for an optical system. We use the optical properties of a one-dimensional nanoslit array for a practical implementation of the study. This method allows to build more generalizable relations between the input parameters of the array and its optical properties, showing a 20-fold improvement for parameters outside the range used for the training. We also show how this model generates the output spectrum from phenomenological relationships between the input parameters and the output spectrum, indicating how it grasps the physical mechanisms of the optical response of the structure.

© 2020 Optical Society of America

Optimization of an H0 photonic crystal nanocavity using machine learning

Ryotaro Abe, Taichi Takeda, Ryo Shiratori, Shinichi Shirakawa, Shota Saito, and Toshihiko Baba
Opt. Lett. 45(2) 319-322 (2020)

Optical circular dichroism engineering in chiral metamaterials utilizing a deep learning network

Zilong Tao, Jie You, Jun Zhang, Xin Zheng, Hengzhu Liu, and Tian Jiang
Opt. Lett. 45(6) 1403-1406 (2020)

Decentralizing machine-learning-based QoT estimation for sliceable optical networks

Tania Panayiotou, Giannis Savva, Ioannis Tomkos, and Georgios Ellinas
J. Opt. Commun. Netw. 12(7) 146-162 (2020)

Optical machine learning with incoherent light and a single-pixel detector

Shuming Jiao, Jun Feng, Yang Gao, Ting Lei, Zhenwei Xie, and Xiaocong Yuan
Opt. Lett. 44(21) 5186-5189 (2019)

All-optical diffractive neural networked terahertz hologram

Dashuang Liao, Ka Fai Chan, Chi Hou Chan, Qingle Zhang, and Haogang Wang
Opt. Lett. 45(10) 2906-2909 (2020)