Chaurasia, A.R.Marwade, V.Singh, M.2026-02-062025APCI 2025 - 2025 International Conference on Advancements in Power, Communication and Intelligent Systems, 2025, Vol., , p. -https://doi.org/10.1109/APCI65531.2025.11136908https://idr.nitk.ac.in/handle/123456789/28608Estimating the effective refractive index of a plasmonic waveguide with high precision is essential for various photonic applications. Traditional analytical and numerical methods often involve extensive computational methods. Deep learning-based approaches have shown promise in improving both accuracy and efficiency. This paper presents a deep learning-based approach for effective refractive index estimation using a hybrid Complex Aware Transformer-Convolutional Neural Network (CAT-CNN) model utilizing convolutional feature extraction, transformer-based attention mechanisms, and squeeze-and-excitation blocks to improve predictive accuracy. Trained on a dataset of plasmonic waveguide parameters at a fixed frequency of 193.2 THz, the model achieves a combined testing R2 score of 0.99978, demonstrating high precision in predicting the real and imaginary parts of the effective refractive index. Our results demonstrate that CAT-CNN achieves state-of-the-art performance in terms of prediction accuracy and computational efficiency. The proposed model has significant implications for the design of high-performance plasmonic sensors and integrated photonic devices. © 2025 IEEE.CNNDeep LearningPlasmonicsRefractive Index PredictionSqueeze-and-ExcitationTransformer