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arXiv · 2504.00203

Fixed-Attention Mechanism for Deep-Learning-Assisted Design of High-Degree-of-Freedom 3D Metamaterials

Huanshu Zhang·Lei Kang·Sawyer D. Campbell·Kaishun Zhang·Douglas H. Werner·Zhaolong Cao
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Abstract

The traditional design approaches for high-degree-of-freedom metamaterials have been computationally intensive and, in many cases, even intractable due to the vast design space. In this work, we introduce a novel fixed-attention mechanism into a deep learning framework to address the computational challenges of metamaterial design. We consider a 3D plasmonic structure composed of gold nanorods characterized by geometric parameters and demonstrate that a Long Short-Term Memory network with a fixed-attention mechanism can improve the prediction accuracy by 48.09% compared to networks without attention. Additionally, we successfully apply this framework for the inverse design of plasmonic metamaterials. Our approach significantly reduces computational costs, opening the door for efficient real-time optimization of complex nanostructures.

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Huanshu Zhang, Lei Kang, Sawyer D. Campbell, Kaishun Zhang, Douglas H. Werner, Zhaolong Cao. 2025-03-31. Fixed-Attention Mechanism for Deep-Learning-Assisted Design of High-Degree-of-Freedom 3D Metamaterials. https://doi.org/10.1364/oe.557837

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