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11th International Symposium on Telecommunication (IST'2024)
Deep Learning for Electromagnetic Metamaterial Inverse Design
Authors :
Ali Nezaratizadeh
1
Seyed Mohammad Hashemi
2
Mohammad Bod
3
1- University of Florida
2- Shahid Rajaee Teacher Training University
3- Shahid Rajaee Teacher Training University
Keywords :
deep learning،metamaterial،metasurface
Abstract :
Designing metamaterials involves computationally intensive tasks, resulting in a time-consuming design process. A deep learning approach is proposed to generate metamaterial designs directly from the input S-parameter diagram. Furthermore, to address the challenge of creating the input diagram, a fine-tuned Large Language Model (LLM) generates the S-parameter diagram based on user input prompts. Compared to traditional neural network methods, the use of an image-based deep learning network enhances the architecture by minimizing the pre and post-processing steps required to transform a metamaterial unitcell image into an numerical array, and vice versa. Simulation results confirm the agreement between the generated metamaterial and the desired input response.
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