Modeling and predicting the beam properties from grating structures using deep neural network

Modeling and predicting the beam properties from grating structures using deep neural network

In this study, the optical properties of light beam coupled out from grating structures are simulated, modeled, and predicted. Gratings with various radius of curvature (R) are designed, where optical properties of light coupled out from the grating are modeled with deep neural network (DNN). After...

Full description

Saved in:
Bibliographic Details
Main Authors: Lim, Yu Dian, Zhao, Peng, Guidoni, Luca, Likforman, Jean-Pierre, Tan, Chuan Seng
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
Engineering
Particle beam optics
Trapped ions
Online Access:https://hdl.handle.net/10356/175559
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:In this study, the optical properties of light beam coupled out from grating structures are simulated, modeled, and predicted. Gratings with various radius of curvature (R) are designed, where optical properties of light coupled out from the grating are modeled with deep neural network (DNN). After omitting the underfitted and overfitted model, the ideally-fitted model accurately predicts the beam waist of light from R = 30 μm with average percentage error (APE) of 7.3%. At the same time, the ideally-fitted model exhibits training/validation loss APE of 2.2%, indicating that the model is well-fitted.

Similar Items