Li Junwei, Yang Chengfu, Qinhua A, Lan Qiusong, Yun Lijun, Xia Yuelong
School of Information Science and Engineering, Yunnan Normal University, Kunming 650500, China.
Department of Education of Yunnan Province, Engineering Research Center of Computer Vision and Intelligent Control Technology, Kunming 650500, China.
ACS Appl Mater Interfaces. 2024 Sep 18;16(37):49673-49686. doi: 10.1021/acsami.4c11972. Epub 2024 Sep 4.
In this paper, a multineural network fusion freestyle metasurface on-demand design method is proposed. The on-demand design method involves rapidly generating corresponding metasurface patterns based on the user-defined spectrum. The generated patterns are then input into a simulator to predict their corresponding S-parameter spectrogram, which is subsequently analyzed against the real S-parameter spectrogram to verify whether the generated metasurface patterns meet the desired requirements. The methodology is based on three neural network models: a Wasserstein Generative Adversarial Network model with a U-net architecture (U-WGAN) for inverse structural design, a Variational Autoencoder (VAE) model for compression, and an LSTM + Attention model for forward S-parameter spectrum prediction validation. The U-WGAN is utilized for on-demand reverse structural design, aiming to rapidly discover high-fidelity metasurface patterns that meet specific electromagnetic spectrum responses. The VAE, as a probabilistic generation model, serves as a bridge, mapping input data to latent space and transforming it into latent variable data, providing crucial input for a forward S-parameter spectrum prediction model. The LSTM + Attention network, acting as a forward S-parameter spectrum prediction model, can accurately and efficiently predict the S-parameter spectrum corresponding to the latent variable data and compare it with the real spectrum. In addition, the digits "0" and "1" are used in the design to represent vacuum and metallic materials, respectively, and a 10 × 10 cell array of freestyle metasurface patterns is constructed. The significance of the research method proposed in this paper lies in the following: (1) The freestyle metasurface design significantly expands the possibility of metamaterial design, enabling the creation of diverse metasurface structures that are difficult to achieve with traditional methods. (2) The on-demand design approach can generate high-fidelity metasurface patterns that meet the expected electromagnetic characteristics and responses. (3) The fusion of multiple neural networks demonstrates high flexibility, allowing for the adjustment of network structures and training methods based on specific design requirements and data characteristics, thus better accommodating different design problems and optimization objectives.
本文提出了一种多神经网络融合的自由形式超表面按需设计方法。该按需设计方法包括根据用户定义的频谱快速生成相应的超表面图案。然后将生成的图案输入模拟器以预测其相应的S参数频谱图，随后将其与真实的S参数频谱图进行分析，以验证生成的超表面图案是否满足期望的要求。该方法基于三个神经网络模型：用于逆结构设计的具有U-net架构的瓦瑟斯坦生成对抗网络模型（U-WGAN）、用于压缩的变分自编码器（VAE）模型以及用于正向S参数频谱预测验证的LSTM+注意力模型。U-WGAN用于按需逆向结构设计，旨在快速发现满足特定电磁频谱响应的高保真超表面图案。VAE作为概率生成模型，充当桥梁，将输入数据映射到潜在空间并将其转换为潜在变量数据，为正向S参数频谱预测模型提供关键输入。LSTM+注意力网络作为正向S参数频谱预测模型，可以准确有效地预测与潜在变量数据对应的S参数频谱，并将其与真实频谱进行比较。此外，在设计中使用数字“0”和“1”分别表示真空和金属材料，并构建了一个10×10单元阵列的自由形式超表面图案。本文提出的研究方法的意义在于：（1）自由形式超表面设计显著扩展了超材料设计的可能性，能够创建传统方法难以实现的多种超表面结构。（2）按需设计方法可以生成满足预期电磁特性和响应的高保真超表面图案。（3）多个神经网络的融合展示了高度的灵活性，允许根据特定的设计要求和数据特征调整网络结构和训练方法，从而更好地适应不同的设计问题和优化目标。
