Ultrahigh-Q chiral resonances empowered by multi-head attention deep learning
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917138964414464 |
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| author | Zhang, Cong Wu, Jiaju Wu, Huazheng Liu, Yufei Yang, Xu Liu, Na Wang, Chaoyang Chen, Peipei Yan, Chenggang Yang, Seng Liu, Xingguang Jiang, Shaowei |
| author_facet | Zhang, Cong Wu, Jiaju Wu, Huazheng Liu, Yufei Yang, Xu Liu, Na Wang, Chaoyang Chen, Peipei Yan, Chenggang Yang, Seng Liu, Xingguang Jiang, Shaowei |
| contents | High quality (Q) factor optical chiral resonators are indispensable for many chiral photonic devices. Designing ultrahigh Q-factors in chiral metasurfaces traditionally relies on extensive parameter scanning, which is time-consuming and inefficient. While deep learning now provides a rapid design alternative, conventional models still face challenges in accurately predicting ultrahigh Q-factor spectral characteristics. In this study, we introduce a multi-head attention network (MuHAN) to accelerate the design of ultrahigh Q-factor optical chiral resonators in bilayer metasurfaces. MuHAN achieves forward spectral predictions in approximately 10ms, thousands of times faster than finite-difference time-domain simulations, boasting 99.85% and 99.9% accuracy for forward and inverse predictions, respectively. By transferring the learned physical principles, we perform inverse design of nanoscale structures with ultrahigh Q-factors (up to 2.9910E5) based on chiral quasi-bound states in the continuum (quasi-BICs) at minimal computational cost. Our rapid design tool, based on MuHAN, enables high-performance encryption imaging, bridging deep learning with high-Q chiral metasurfaces for advanced sensing, laser, and detection applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ultrahigh-Q chiral resonances empowered by multi-head attention deep learning Zhang, Cong Wu, Jiaju Wu, Huazheng Liu, Yufei Yang, Xu Liu, Na Wang, Chaoyang Chen, Peipei Yan, Chenggang Yang, Seng Liu, Xingguang Jiang, Shaowei Optics 35Q62, 68T07, 94A08 I.2.10; I.2.6 High quality (Q) factor optical chiral resonators are indispensable for many chiral photonic devices. Designing ultrahigh Q-factors in chiral metasurfaces traditionally relies on extensive parameter scanning, which is time-consuming and inefficient. While deep learning now provides a rapid design alternative, conventional models still face challenges in accurately predicting ultrahigh Q-factor spectral characteristics. In this study, we introduce a multi-head attention network (MuHAN) to accelerate the design of ultrahigh Q-factor optical chiral resonators in bilayer metasurfaces. MuHAN achieves forward spectral predictions in approximately 10ms, thousands of times faster than finite-difference time-domain simulations, boasting 99.85% and 99.9% accuracy for forward and inverse predictions, respectively. By transferring the learned physical principles, we perform inverse design of nanoscale structures with ultrahigh Q-factors (up to 2.9910E5) based on chiral quasi-bound states in the continuum (quasi-BICs) at minimal computational cost. Our rapid design tool, based on MuHAN, enables high-performance encryption imaging, bridging deep learning with high-Q chiral metasurfaces for advanced sensing, laser, and detection applications. |
| title | Ultrahigh-Q chiral resonances empowered by multi-head attention deep learning |
| topic | Optics 35Q62, 68T07, 94A08 I.2.10; I.2.6 |
| url | https://arxiv.org/abs/2512.10798 |