深度学习设计的总传播

Bei Wu , Zhan-Lei Hao , Jin-Hui Chen , Qiao-Liang Bao , Yi-Neng Liu , Huan-Yang Chen
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引用次数: 2

摘要

全传输在提高效率和波前控制方面起着重要的作用,在光学薄膜和信号传输等许多应用中都取得了很大的进展。因此,人们研究了以变换光学为代表的许多传统物理方法来实现全透射。然而,这些方法对光子结构的尺寸有严格的限制,且计算复杂。在这里,我们利用深度学习来实现这一目标。在深度学习中,数据驱动的预测和设计是由人工神经网络(ann)进行的,它为大数据集问题提供了一个方便的架构。以多层堆叠的传输特性为例,说明了如何利用人工神经网络设计光学材料。训练后的网络直接建立了光学材料到透射光谱的映射关系,实现了给定参数空间下总透射光谱的正向预测和材料逆设计。我们的工作为基于深度学习的具有特殊性能的光学材料设计铺平了道路。
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Total transmission from deep learning designs

Total transmission plays an important role in efficiency improvement and wavefront control, and has made great progress in many applications, such as the optical film and signal transmission. Therefore, many traditional physical methods represented by transformation optics have been studied to achieve total transmission. However, these methods have strict limitations on the size of the photonic structure, and the calculation is complex. Here, we exploit deep learning to achieve this goal. In deep learning, the data-driven prediction and design are carried out by artificial neural networks (ANNs), which provide a convenient architecture for large dataset problems. By taking the transmission characteristic of the multi-layer stacks as an example, we demonstrate how optical materials can be designed by using ANNs. The trained network directly establishes the mapping from optical materials to transmission spectra, and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space. Our work paves the way for the optical material design with special properties based on deep learning.

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来源期刊
Journal of Electronic Science and Technology
Journal of Electronic Science and Technology Engineering-Electrical and Electronic Engineering
CiteScore
4.30
自引率
0.00%
发文量
1362
审稿时长
99 days
期刊介绍: JEST (International) covers the state-of-the-art achievements in electronic science and technology, including the most highlight areas: ¨ Communication Technology ¨ Computer Science and Information Technology ¨ Information and Network Security ¨ Bioelectronics and Biomedicine ¨ Neural Networks and Intelligent Systems ¨ Electronic Systems and Array Processing ¨ Optoelectronic and Photonic Technologies ¨ Electronic Materials and Devices ¨ Sensing and Measurement ¨ Signal Processing and Image Processing JEST (International) is dedicated to building an open, high-level academic journal supported by researchers, professionals, and academicians. The Journal has been fully indexed by Ei INSPEC and has published, with great honor, the contributions from more than 20 countries and regions in the world.
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