用串联生成网络提高高自由度元原子设计精度和速度

IF 6.7 1区 物理与天体物理 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Haolan Yang, Chuanchuan Yang* and Hongbin Li, 
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引用次数: 0

摘要

传统的超表面设计方法很大程度上依赖于研究人员的先验知识和使用全波模拟的迭代试错方法,导致过程冗长且效率低下。深度学习技术,如串联神经网络(tnn)和生成网络,在解决反设计问题方面显示出相当大的希望。然而,TNN在创建高自由度结构方面面临挑战,并且在逆问题中忽略了一对多映射的学习。消噪扩散概率模型(DDPM)虽然在生成精度和质量上优于其他生成网络,但存在结构生成缓慢的问题。为了实现高精度、高效率的高自由度元原子设计,提出了一种新的元表面设计方法——串联生成网络(TGN)。TGN构造一个原始的概率生成模型,并从概率空间中抽样生成自由形式的元原子。tgn生成的模式经过验证可以产生匹配的透射率,平均绝对误差为0.0356,与DDPM和TNN相比分别降低38%和86%。TGN的生成速度是DDPM的2990倍。通过采用第一个概率生成模型进行元表面设计,TGN为逆向设计的深度学习开辟了新的途径,为设计具有所需电磁特性的复杂元原子结构提供了一种快速准确的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing High-Degree-of-Freedom Meta-Atom Design Precision and Speed with a Tandem Generative Network

Enhancing High-Degree-of-Freedom Meta-Atom Design Precision and Speed with a Tandem Generative Network

Traditional metasurface design approaches largely rely on the prior knowledge of researchers and iterative trial-and-error methods using full-wave simulations, resulting in lengthy and inefficient processes. Deep-learning techniques, such as tandem neural networks (TNNs) and generative networks, show considerable promise in addressing the inverse-design problem. However, TNN faces challenges in creating high-freedom structures and neglects learning one-to-many mappings in inverse problems. The denoising diffusion probabilistic model (DDPM), while superior to other generative networks in generation precision and quality, is hindered by slow structure generation. This paper proposes a novel metasurface design method called the tandem generative network (TGN) to realize accurate and efficient high-degree-of-freedom meta-atom design. TGN constructs an original probabilistic generative model and generates free-form meta-atoms by sampling from the probability space. TGN-generated patterns are validated to produce matching transmittance with an average mean absolute error of 0.0356, achieving decreases of 38% and 86% compared to DDPM and TNN, respectively. Furthermore, the generation speed of TGN is 2990 times faster than that of DDPM. By employing the first probabilistic generative model for metasurface design, TGN paves new avenues in deep learning for inverse design, providing a swift and accurate means to design complex meta-atom structures with desired electromagnetic properties.

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来源期刊
ACS Photonics
ACS Photonics NANOSCIENCE & NANOTECHNOLOGY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
11.90
自引率
5.70%
发文量
438
审稿时长
2.3 months
期刊介绍: Published as soon as accepted and summarized in monthly issues, ACS Photonics will publish Research Articles, Letters, Perspectives, and Reviews, to encompass the full scope of published research in this field.
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