Single Mode rib waveguide design using Machine Learning techniques

\nThis work aim to determine a Single Mode (SM) Silicon-On-Insulator (SOI) rib waveguide using Machine learning (ML) techniques, which learn automatically by matching the input data with the target property. Random Forest (RF) is the ML algorithm used in this work. The accuracy of the model reaches 99% by using R2 score. The device is a rib waveguide based on Silicon (Si), with a cladding made of Silica (SiO2). The results obtained illustrate the conditions for SM, with the width and the rib etch-depth found to be relatively smaller compared to the total thickness. The ML approach have proven to be quick and effective regarding this problem.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-10-31
DOI
https://doi.org/10.1051/epjconf/202430901010/pdf
Primary Topic
Photonic and Optical Devices
Type
article
Field-Weighted Citation Impact
0.00

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article

Single Mode rib waveguide design using Machine Learning techniques

Springer Link (Chiba Institute of Technology)
Photonic and Optical Devices
article

Single Mode rib waveguide design using Machine Learning techniques

article en

Abstract

\nThis work aim to determine a Single Mode (SM) Silicon-On-Insulator (SOI) rib waveguide using Machine learning (ML) techniques, which learn automatically by matching the input data with the target property. Random Forest (RF) is the ML algorithm used in this work. The accuracy of the model reaches 99% by using R2 score. The device is a rib waveguide based on Silicon (Si), with a cladding made of Silica (SiO2). The results obtained illustrate the conditions for SM, with the width and the rib etch-depth found to be relatively smaller compared to the total thickness. The ML approach have proven to be quick and effective regarding this problem.\n

Springer Link (Chiba Institute of Technology)
European Commission
Life in Land
Openalex Percentile: Top 71%
Photonic and Optical Devices
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Single Mode rib waveguide design using Machine Learning techniques · Springer Link (Chiba Institute of Technology) (2024) | TGRS Research Map | TGRS