Parametric data-driven optimization approach on plasmonic based ring resonator. (2022)

• Record Type: Journal Article
• Authors: Sharma, Priyanka; Zafar, Rukhsar; Pandey, Rahul
• Is Part Of: Materials today. Volume 66:Part 8(2022)
• Page Start: 3640
• Page End: 3643
• Publication Date: 2022
• Subjects: Plasmonics -- Waveguide -- Ring resonator -- Machine learning -- Data driven optimization; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.07.183
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: In this paper, a plasmonic based ring resonator is being investigated. The metallic portion of plasmonic waveguide is chosen to be silver while the insulating material is air. The structure is numerically analyzed through Finite Difference in Time Domain (FDTD) method. To accurately model the relationship between the design parameter (radius of the ring) and the resonance condition of the resonator, a Machine Learning (ML) approach is applied. The ML model is trained on a dataset and tested for obtained results. The simulated results show that the proposed model is able to achieve the accuracy of prediction. The data driven optimization technique is being used for finding out the appropriate resonance condition of ring resonator. The proposed model is able to achieve good prediction accuracy (>90 %). The predicted result can be utilized in designing desired plasmonic devices like sensors, splitter, optical gates etc.