Strategical Deep Learning for Photonic Bound States in the Continuum

AbstractResonance 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 endtoend deep learning approaches generally fail to predict resonances with highqualityfactors (Qfactor) due to their intrinsic nonlinearity and complexity. Here, a resonance informed deep learning (RIDL) strategy for rapid and accurate prediction of the optical response for ultrahighQfactor resonances is introduced. By incorporating the resonance information into the deep learning algorithm, the RIDL strategy achieves a highaccuracy 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 Qfactor is applied. The predicted and measured angleresolved 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.

Ma, Xuezhi||Ma, Yuan||Cunha, Preston||Liu, Qiushi||Kudtarkar, Kaushik||Xu, Da||Wang, Jiafei||Chen, Yixin||Wong, Zi Jing||Liu, Ming||Hipwell, M Cynthia||Lan, Shoufeng

Strategical Deep Learning for Photonic Bound States in the Continuum Academic Article