Stacking integrated learning-based inverse design of four-nanopore high-Q all-dielectric metasurface

Abstract In this paper, we establish a four-nanophole hollowed out all-dielectric metasurface structure as the object of reverse design, which can realize many types of high Q-value Fano resonance effects through dynamic tuning of a single structure, with Q value up to 4401. We used the time domain finite difference method for data collection, and then select appropriate models according to the characteristics of the data from the perspective of the data itself. We select in small sample prediction excellent support vector regression (SVR), gradient promotion decision tree (GBDT), prediction generalization performance and stability of high random forest (RF) as a base learners, with good nonlinear fitting ability BP neural network (BPNN) yuan learners, establish a fusion model based on Stacking integrated learning strategy. Based on this fusion model, the multi-parameter all-dielectric metasurface structure is reverse designed instead. The results show that the proposed method has high prediction accuracy with an absolute average error of only 0.0043, and excellent average accuracy of 82.5%, 77.9% and 14% compared with the combined model GBDT-SVM-BP, GBDT-RF-BP, and RF-SVM-BP. This study provides a new perspective on the reverse design of all-dielectric metasurface structures.