Robust Hotspot Detection in Photonic Crystals Using a Hybrid Capsule–Transformer Deep Learning Model

This study introduces a novel hybrid deep learning framework to enhance hotspot detection in photonic crystal design, addressing challenges in accuracy and overfitting. By integrating Capsule Networks (CapsNet) and transformer architectures with ensemble learning and data augmentation, the proposed approach optimizes the identification of wavelength propagation discrepancies in photonic structures. Experiments conducted on the ICCAD-2012 benchmark dataset demonstrate that the ensemble model achieves 90% test accuracy, outperforming standalone CapsNet and transformer models while reducing overfitting. The model also demonstrates strong classification consistency, with an F1-score of 90% and a G-mean of 90%, indicating robust performance across precision–recall balance and class-wise sensitivity–specificity harmony. The framework’s success in balancing performance and generalization highlights its potential to streamline photonic device design for applications in sensing, telecommunications, and energy harvesting. This work bridges advanced machine learning techniques with photonic engineering, offering a scalable and efficient solution for complex light-matter interaction analysis.