Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators

Sebastian Valencia-Garzon (), Esteban Gonzalez-Valencia, Nelson Gómez-Cardona, Andres Calvo-Salcedo, J. A. Jaramillo-Villegas, Jorge Montoya-Cardona and Erick Reyes-Vera
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Sebastian Valencia-Garzon: Department of Systems, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia 2 Department of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia
Esteban Gonzalez-Valencia: Department of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia
Nelson Gómez-Cardona: Department of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia
Andres Calvo-Salcedo: Faculty of Engineering, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
J. A. Jaramillo-Villegas: Faculty of Engineering, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
Jorge Montoya-Cardona: Department of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia
Erick Reyes-Vera: Department of Electronics and Telecommunications, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia

Data, 2024, vol. 10, issue 1, 1-11

Abstract: 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.

Keywords: integrated optics; micro-ring resonator; optical resonator; dataset; silicon photonics (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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