Strategical Deep Learning for Photonic Bound States in the Continuum
Resonance is instrumental in modern optics and photonics. While one can use numerical simulations to sweep geometric and material parameters of optical structures, these simulations usually require considerably long calculation time and substantial computational resources. Such requirements signific...
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| Vydáno v: | Laser & photonics reviews Ročník 16; číslo 10 |
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| Jazyk: | angličtina |
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01.10.2022
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| ISSN: | 1863-8880, 1863-8899 |
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| Abstract | Resonance is instrumental in modern optics and photonics. While one can use numerical simulations to sweep geometric and material parameters of optical structures, these simulations usually require considerably long calculation time and substantial computational resources. Such requirements significantly limit their applicability in the inverse design of structures with desired resonances. The recent introduction of artificial intelligence allows for faster spectra predictions of resonance. However, even with relatively large training datasets, current end‐to‐end deep learning approaches generally fail to predict resonances with high‐quality‐factors (Q‐factor) due to their intrinsic non‐linearity and complexity. Here, a resonance informed deep learning (RIDL) strategy for rapid and accurate prediction of the optical response for ultra‐high‐Q‐factor resonances is introduced. By incorporating the resonance information into the deep learning algorithm, the RIDL strategy achieves a high‐accuracy prediction of reflection spectra and photonic band structures while using a comparatively small training dataset. Further, the RIDL strategy to develop an inverse design algorithm for designing a bound state in the continuum (BIC) with infinite Q‐factor is applied. The predicted and measured angle‐resolved band structures of this device show minimal differences. The RIDL strategy is expected to be applied to many other physical phenomena such as Gaussian and Lorentzian resonances.
Photonic bound states in the continuum (BICs) are instrumental for many applications, but designing them is extremely resource‐consuming due to their high‐quality nature. With the help of the adaptive data acquisition (ADA) method, which reduced ∼99% of simulation workload, the resonance‐informed deep learning (RIDL) strategy significantly increased the prediction accuracy and reduced computational time for designing BICs with ultra‐high‐quality‐factors. |
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| AbstractList | Resonance is instrumental in modern optics and photonics. While one can use numerical simulations to sweep geometric and material parameters of optical structures, these simulations usually require considerably long calculation time and substantial computational resources. Such requirements significantly limit their applicability in the inverse design of structures with desired resonances. The recent introduction of artificial intelligence allows for faster spectra predictions of resonance. However, even with relatively large training datasets, current end‐to‐end deep learning approaches generally fail to predict resonances with high‐quality‐factors (Q‐factor) due to their intrinsic non‐linearity and complexity. Here, a resonance informed deep learning (RIDL) strategy for rapid and accurate prediction of the optical response for ultra‐high‐Q‐factor resonances is introduced. By incorporating the resonance information into the deep learning algorithm, the RIDL strategy achieves a high‐accuracy prediction of reflection spectra and photonic band structures while using a comparatively small training dataset. Further, the RIDL strategy to develop an inverse design algorithm for designing a bound state in the continuum (BIC) with infinite Q‐factor is applied. The predicted and measured angle‐resolved band structures of this device show minimal differences. The RIDL strategy is expected to be applied to many other physical phenomena such as Gaussian and Lorentzian resonances.
Photonic bound states in the continuum (BICs) are instrumental for many applications, but designing them is extremely resource‐consuming due to their high‐quality nature. With the help of the adaptive data acquisition (ADA) method, which reduced ∼99% of simulation workload, the resonance‐informed deep learning (RIDL) strategy significantly increased the prediction accuracy and reduced computational time for designing BICs with ultra‐high‐quality‐factors. Resonance is instrumental in modern optics and photonics. While one can use numerical simulations to sweep geometric and material parameters of optical structures, these simulations usually require considerably long calculation time and substantial computational resources. Such requirements significantly limit their applicability in the inverse design of structures with desired resonances. The recent introduction of artificial intelligence allows for faster spectra predictions of resonance. However, even with relatively large training datasets, current end‐to‐end deep learning approaches generally fail to predict resonances with high‐quality‐factors (Q‐factor) due to their intrinsic non‐linearity and complexity. Here, a resonance informed deep learning (RIDL) strategy for rapid and accurate prediction of the optical response for ultra‐high‐Q‐factor resonances is introduced. By incorporating the resonance information into the deep learning algorithm, the RIDL strategy achieves a high‐accuracy prediction of reflection spectra and photonic band structures while using a comparatively small training dataset. Further, the RIDL strategy to develop an inverse design algorithm for designing a bound state in the continuum (BIC) with infinite Q‐factor is applied. The predicted and measured angle‐resolved band structures of this device show minimal differences. The RIDL strategy is expected to be applied to many other physical phenomena such as Gaussian and Lorentzian resonances. |
| Author | Ma, Xuezhi Xu, Da Cunha, Preston Liu, Ming Hipwell, M. Cynthia Ma, Yuan Wang, Jiafei Lan, Shoufeng Kudtarkar, Kaushik Chen, Yixin Liu, Qiushi Wong, Zi Jing |
| Author_xml | – sequence: 1 givenname: Xuezhi surname: Ma fullname: Ma, Xuezhi organization: Texas A&M University – sequence: 2 givenname: Yuan surname: Ma fullname: Ma, Yuan organization: The Hong Kong Polytechnic University – sequence: 3 givenname: Preston surname: Cunha fullname: Cunha, Preston organization: Texas A&M University – sequence: 4 givenname: Qiushi surname: Liu fullname: Liu, Qiushi organization: University of California – sequence: 5 givenname: Kaushik surname: Kudtarkar fullname: Kudtarkar, Kaushik organization: Texas A&M University – sequence: 6 givenname: Da surname: Xu fullname: Xu, Da organization: University of California – sequence: 7 givenname: Jiafei surname: Wang fullname: Wang, Jiafei organization: Texas A&M University – sequence: 8 givenname: Yixin surname: Chen fullname: Chen, Yixin organization: Texas A&M University – sequence: 9 givenname: Zi Jing surname: Wong fullname: Wong, Zi Jing organization: Texas A&M University – sequence: 10 givenname: Ming surname: Liu fullname: Liu, Ming organization: University of California – sequence: 11 givenname: M. Cynthia surname: Hipwell fullname: Hipwell, M. Cynthia organization: Texas A&M University – sequence: 12 givenname: Shoufeng orcidid: 0000-0003-2108-6774 surname: Lan fullname: Lan, Shoufeng email: shoufeng@tamu.edu organization: Texas A&M University |
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| SubjectTerms | Algorithms Artificial intelligence bound states in the continuum (BICs) Computer simulation Datasets Deep learning Fano resonance high‐quality‐factor resonance design Inverse design Machine learning Photonics resonance‐informed‐deep‐learning (RIDL) strategy semi‐supervised deep learning Spectra Training |
| Title | Strategical Deep Learning for Photonic Bound States in the Continuum |
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