Parametric optimization of the slot waveguide characteristics using a machine-learning approach
Abstract Slot waveguides provide high electric field amplitude and optical power in low-index materials that are not possible with conventional waveguides. This specific property of the slot waveguide provides interaction between active material and electric field, which led to many interesting appl...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-07521-5 |
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| _version_ | 1849769346566455296 |
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| author | Yadvendra Singh Suraj Jena Harish Subbaraman |
| author_facet | Yadvendra Singh Suraj Jena Harish Subbaraman |
| author_sort | Yadvendra Singh |
| collection | DOAJ |
| description | Abstract Slot waveguides provide high electric field amplitude and optical power in low-index materials that are not possible with conventional waveguides. This specific property of the slot waveguide provides interaction between active material and electric field, which led to many interesting applications, such as optical amplification, optical switching, and optical detection in integrated photonics. In the present work, we combine machine learning (ML) algorithms and finite element simulation to predict the power confinement ( $$\hbox {P}_{conf}$$ ) and mode effective index ( $$\hbox {n}_{eff}$$ ) of slot waveguides with respect to geometric parameters such as gap, slab width, and slab height. Three different ML techniques, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF), were tested to compute performance parameters for the slot waveguide. The RF method outperformed the other two with mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( $$\hbox {R}^{2}$$ ), and Nash–Sutcliffe efficiency (NSE) values corresponding $$\hbox {n}_{eff}$$ and $$\hbox {P}_{conf}$$ as 0.007, 0.054, 0.961, and 0.960, and 0.129, 0.185, 0.998, and 0.998, respectively. Thus, providing a useful ML methodology for efficient optimization of slot waveguide structures for future applications. |
| format | Article |
| id | doaj-art-e410a72c441c48f08047771ef412438e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e410a72c441c48f08047771ef412438e2025-08-20T03:03:27ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-07521-5Parametric optimization of the slot waveguide characteristics using a machine-learning approachYadvendra Singh0Suraj Jena1Harish Subbaraman2School of Electrical Engineering and Computer Science, Oregon State UniversityCollege of Agricultural Science, Oregon State UniversitySchool of Electrical Engineering and Computer Science, Oregon State UniversityAbstract Slot waveguides provide high electric field amplitude and optical power in low-index materials that are not possible with conventional waveguides. This specific property of the slot waveguide provides interaction between active material and electric field, which led to many interesting applications, such as optical amplification, optical switching, and optical detection in integrated photonics. In the present work, we combine machine learning (ML) algorithms and finite element simulation to predict the power confinement ( $$\hbox {P}_{conf}$$ ) and mode effective index ( $$\hbox {n}_{eff}$$ ) of slot waveguides with respect to geometric parameters such as gap, slab width, and slab height. Three different ML techniques, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF), were tested to compute performance parameters for the slot waveguide. The RF method outperformed the other two with mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( $$\hbox {R}^{2}$$ ), and Nash–Sutcliffe efficiency (NSE) values corresponding $$\hbox {n}_{eff}$$ and $$\hbox {P}_{conf}$$ as 0.007, 0.054, 0.961, and 0.960, and 0.129, 0.185, 0.998, and 0.998, respectively. Thus, providing a useful ML methodology for efficient optimization of slot waveguide structures for future applications.https://doi.org/10.1038/s41598-025-07521-5 |
| spellingShingle | Yadvendra Singh Suraj Jena Harish Subbaraman Parametric optimization of the slot waveguide characteristics using a machine-learning approach Scientific Reports |
| title | Parametric optimization of the slot waveguide characteristics using a machine-learning approach |
| title_full | Parametric optimization of the slot waveguide characteristics using a machine-learning approach |
| title_fullStr | Parametric optimization of the slot waveguide characteristics using a machine-learning approach |
| title_full_unstemmed | Parametric optimization of the slot waveguide characteristics using a machine-learning approach |
| title_short | Parametric optimization of the slot waveguide characteristics using a machine-learning approach |
| title_sort | parametric optimization of the slot waveguide characteristics using a machine learning approach |
| url | https://doi.org/10.1038/s41598-025-07521-5 |
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