Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators
This study focuses on the analysis of the spectral response of all-pass micro-ring resonators (MRRs), which are essential in photonic device applications such as telecommunications, sensing, and optical frequency comb generation. The aim of this work is to generate a synthetic dataset that explores...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2306-5729/10/1/3 |
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| author | Sebastian Valencia-Garzon Esteban Gonzalez-Valencia Nelson Gómez-Cardona Andres Calvo-Salcedo J. A. Jaramillo-Villegas Jorge Montoya-Cardona Erick Reyes-Vera |
| author_facet | Sebastian Valencia-Garzon Esteban Gonzalez-Valencia Nelson Gómez-Cardona Andres Calvo-Salcedo J. A. Jaramillo-Villegas Jorge Montoya-Cardona Erick Reyes-Vera |
| author_sort | Sebastian Valencia-Garzon |
| collection | DOAJ |
| description | This study focuses on the analysis of the spectral response of all-pass micro-ring resonators (MRRs), which are essential in photonic device applications such as telecommunications, sensing, and optical frequency comb generation. The aim of this work is to generate a synthetic dataset that explores the spectral characteristics of the expected transmission spectra of MRRs by varying their structural parameters. Using numerical simulations, the dataset will allow the optimization of MRR performance metrics such as free spectral range (FSR), full width at half maximum (FWHM), and quality factor (Q-factor). The results confirm that variations in geometric configurations can significantly affect MRR performance, and the dataset provides valuable insights into the optimization process. Furthermore, machine learning techniques can be applied to the dataset to automate and improve the design process, reducing simulation times and increasing accuracy. This work contributes to the development of photonic devices by providing a broad dataset for further analysis and optimization. |
| format | Article |
| id | doaj-art-8fb28f48c1614237b3819cb49404836c |
| institution | Kabale University |
| issn | 2306-5729 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-8fb28f48c1614237b3819cb49404836c2025-01-24T13:28:32ZengMDPI AGData2306-57292024-12-01101310.3390/data10010003Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring ResonatorsSebastian Valencia-Garzon0Esteban Gonzalez-Valencia1Nelson Gómez-Cardona2Andres Calvo-Salcedo3J. A. Jaramillo-Villegas4Jorge Montoya-Cardona5Erick Reyes-Vera6Department of Systems, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia 2 Department of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, ColombiaDepartment of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, ColombiaDepartment of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, ColombiaFaculty of Engineering, Universidad Tecnológica de Pereira, Pereira 660003, ColombiaFaculty of Engineering, Universidad Tecnológica de Pereira, Pereira 660003, ColombiaDepartment of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, ColombiaDepartment of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, ColombiaThis study focuses on the analysis of the spectral response of all-pass micro-ring resonators (MRRs), which are essential in photonic device applications such as telecommunications, sensing, and optical frequency comb generation. The aim of this work is to generate a synthetic dataset that explores the spectral characteristics of the expected transmission spectra of MRRs by varying their structural parameters. Using numerical simulations, the dataset will allow the optimization of MRR performance metrics such as free spectral range (FSR), full width at half maximum (FWHM), and quality factor (Q-factor). The results confirm that variations in geometric configurations can significantly affect MRR performance, and the dataset provides valuable insights into the optimization process. Furthermore, machine learning techniques can be applied to the dataset to automate and improve the design process, reducing simulation times and increasing accuracy. This work contributes to the development of photonic devices by providing a broad dataset for further analysis and optimization.https://www.mdpi.com/2306-5729/10/1/3integrated opticsmicro-ring resonatoroptical resonatordatasetsilicon photonics |
| spellingShingle | Sebastian Valencia-Garzon Esteban Gonzalez-Valencia Nelson Gómez-Cardona Andres Calvo-Salcedo J. A. Jaramillo-Villegas Jorge Montoya-Cardona Erick Reyes-Vera Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators Data integrated optics micro-ring resonator optical resonator dataset silicon photonics |
| title | Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators |
| title_full | Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators |
| title_fullStr | Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators |
| title_full_unstemmed | Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators |
| title_short | Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators |
| title_sort | synthetic dataset for analyzing geometry dependent optical properties of all pass micro ring resonators |
| topic | integrated optics micro-ring resonator optical resonator dataset silicon photonics |
| url | https://www.mdpi.com/2306-5729/10/1/3 |
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