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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.09742 |
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| _version_ | 1866915362823471104 |
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| author | Shen, H. Zhao, C. Y. |
| author_facet | Shen, H. Zhao, C. Y. |
| contents | The research on sensing the sensitivity of the light field in the whispering gallery mode (WGM) to the micro-cavity environment has already appeared, which uses the frequency shift of the light field in the WGM or the sensitivity of the resonance peak frequency shift. Multi-mode comb teeth of optical frequency comb(OFC) generated by nonlinear micro-cavity have excellent sensitivity to micro-cavity environment, and they have more sensitivity degrees of freedom compared with WGM light field (the strength of each comb tooth can be influenced by micro-cavity environment). The influence of different substances on the environmental parameters of micro-cavity is complex and nonlinear, so we use machine learning method to automatically extract the spectrum characteristics, the average accuracy of single-parameter identification attains to 99.5%, and the average accuracy of double parameter identification attains to 97.0%. Based on the integration of micro-cavity OFC and wave-guide coupling structure, we propose an set of fluid characteristics detection integrated device in theoretically. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09742 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The convolutional neural networks for analysing the micro-cavity array multi-mode quantum frequency comb spectrum features Shen, H. Zhao, C. Y. Optics The research on sensing the sensitivity of the light field in the whispering gallery mode (WGM) to the micro-cavity environment has already appeared, which uses the frequency shift of the light field in the WGM or the sensitivity of the resonance peak frequency shift. Multi-mode comb teeth of optical frequency comb(OFC) generated by nonlinear micro-cavity have excellent sensitivity to micro-cavity environment, and they have more sensitivity degrees of freedom compared with WGM light field (the strength of each comb tooth can be influenced by micro-cavity environment). The influence of different substances on the environmental parameters of micro-cavity is complex and nonlinear, so we use machine learning method to automatically extract the spectrum characteristics, the average accuracy of single-parameter identification attains to 99.5%, and the average accuracy of double parameter identification attains to 97.0%. Based on the integration of micro-cavity OFC and wave-guide coupling structure, we propose an set of fluid characteristics detection integrated device in theoretically. |
| title | The convolutional neural networks for analysing the micro-cavity array multi-mode quantum frequency comb spectrum features |
| topic | Optics |
| url | https://arxiv.org/abs/2404.09742 |