Analysis of Photonic Circuit Losses with Machine Learning Techniques
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915354836467712 |
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| author | Utama, Adrian Nugraha Goh, Simon Chun Kiat Hongyu, Li Xiangyu, Wang Yanyan, Zhou Leong, Victor Mukherjee, Manas |
| author_facet | Utama, Adrian Nugraha Goh, Simon Chun Kiat Hongyu, Li Xiangyu, Wang Yanyan, Zhou Leong, Victor Mukherjee, Manas |
| contents | Low-loss waveguides enable efficient light delivery in photonic circuits, which are essential for high-speed optical communications and scalable implementations of photonic quantum technologies. We study the effects of several fabrication and experimental parameters on the waveguide losses of a silicon nitride integrated photonics platform using various machine learning techniques. Compared to more complex machine learning algorithms, our results show that a simple linear regression model with variable selection offers a lower prediction error with high interpretability. The high interpretability, along with our domain knowledge, led to unique identification of fabrication process dependencies on the final outcome. With these insights, we show that by improving the process flow, it is possible to improve the loss by mitigating the cause in a real experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analysis of Photonic Circuit Losses with Machine Learning Techniques Utama, Adrian Nugraha Goh, Simon Chun Kiat Hongyu, Li Xiangyu, Wang Yanyan, Zhou Leong, Victor Mukherjee, Manas Optics Applied Physics Data Analysis, Statistics and Probability Low-loss waveguides enable efficient light delivery in photonic circuits, which are essential for high-speed optical communications and scalable implementations of photonic quantum technologies. We study the effects of several fabrication and experimental parameters on the waveguide losses of a silicon nitride integrated photonics platform using various machine learning techniques. Compared to more complex machine learning algorithms, our results show that a simple linear regression model with variable selection offers a lower prediction error with high interpretability. The high interpretability, along with our domain knowledge, led to unique identification of fabrication process dependencies on the final outcome. With these insights, we show that by improving the process flow, it is possible to improve the loss by mitigating the cause in a real experiment. |
| title | Analysis of Photonic Circuit Losses with Machine Learning Techniques |
| topic | Optics Applied Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2506.17999 |