Browsing by Author "Marwade, V."

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Complex Aware Transformer-CNN for Refractive Index Prediction in Plasmonic Waveguide
(Institute of Electrical and Electronics Engineers Inc., 2025) Chaurasia, A.R.; Marwade, V.; Singh, M.
Estimating 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. Â© 2025 IEEE.