Photonic Micro-ring Resonator Design and Analysis using Machine Learning Techniques

Assylkhan Nurgali; Carlo Molardi; Bikash Nakarmi; Ikechi A. Ukaegbu

doi:10.1117/12.2691422

Photonic Micro-ring Resonator Design and Analysis using Machine Learning Techniques

Assylkhan Nurgali, Carlo Molardi, Bikash Nakarmi, Ikechi A. Ukaegbu

School of Engineering and Digital Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Micro-ring resonators (MRRs) have emerged as vital components in photonic applications, offering precise control of light at the nanoscale. Achieving optimal MRR design parameters is crucial for maximizing their performance in high-speed applications. This study aims to employ feature engineering and supervised machine learning (ML) techniques to comprehensively analyze MRRs. This includes impact of change in MRR design geometries, such as radius, coupling geometry, waveguide properties to key MRR output parameters, including the quality factor, full width at half maximum (FWHM), rise/fall time, and free spectral range (FSR). By utilizing results of over 1000 simulations in Lumerical, as well as incorporating the theoretical knowledge of MRRs, the study seeks to build highly accurate predictive model.

Original language	English
Title of host publication	Silicon Photonics XIX
Editors	Graham T. Reed, Andrew P. Knights
Publisher	SPIE
ISBN (Electronic)	9781510670426
DOIs	https://doi.org/10.1117/12.2691422
Publication status	Published - 2024
Event	Silicon Photonics XIX 2024 - San Francisco, United States Duration: Jan 29 2024 → Jan 31 2024

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	12891
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Silicon Photonics XIX 2024
Country/Territory	United States
City	San Francisco
Period	1/29/24 → 1/31/24

Keywords

Lumerical simulations
MRR design parameters
MRR design parameters
Supervised machine learning

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2691422

Cite this

Photonic Micro-ring Resonator Design and Analysis using Machine Learning Techniques. / Nurgali, Assylkhan; Molardi, Carlo; Nakarmi, Bikash et al.
Silicon Photonics XIX. ed. / Graham T. Reed; Andrew P. Knights. SPIE, 2024. 128910O (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12891).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nurgali, A, Molardi, C, Nakarmi, B & Ukaegbu, IA 2024, Photonic Micro-ring Resonator Design and Analysis using Machine Learning Techniques. in GT Reed & AP Knights (eds), Silicon Photonics XIX., 128910O, Proceedings of SPIE - The International Society for Optical Engineering, vol. 12891, SPIE, Silicon Photonics XIX 2024, San Francisco, United States, 1/29/24. https://doi.org/10.1117/12.2691422

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title = "Photonic Micro-ring Resonator Design and Analysis using Machine Learning Techniques",

abstract = "Micro-ring resonators (MRRs) have emerged as vital components in photonic applications, offering precise control of light at the nanoscale. Achieving optimal MRR design parameters is crucial for maximizing their performance in high-speed applications. This study aims to employ feature engineering and supervised machine learning (ML) techniques to comprehensively analyze MRRs. This includes impact of change in MRR design geometries, such as radius, coupling geometry, waveguide properties to key MRR output parameters, including the quality factor, full width at half maximum (FWHM), rise/fall time, and free spectral range (FSR). By utilizing results of over 1000 simulations in Lumerical, as well as incorporating the theoretical knowledge of MRRs, the study seeks to build highly accurate predictive model.",

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AB - Micro-ring resonators (MRRs) have emerged as vital components in photonic applications, offering precise control of light at the nanoscale. Achieving optimal MRR design parameters is crucial for maximizing their performance in high-speed applications. This study aims to employ feature engineering and supervised machine learning (ML) techniques to comprehensively analyze MRRs. This includes impact of change in MRR design geometries, such as radius, coupling geometry, waveguide properties to key MRR output parameters, including the quality factor, full width at half maximum (FWHM), rise/fall time, and free spectral range (FSR). By utilizing results of over 1000 simulations in Lumerical, as well as incorporating the theoretical knowledge of MRRs, the study seeks to build highly accurate predictive model.

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ER -

Photonic Micro-ring Resonator Design and Analysis using Machine Learning Techniques

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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