用CNN-LSTM组合提升多层薄膜逆向设计精度与速度
Improving the performance of optical inverse design of multilayer thin films using CNN-LSTM tandem neural networks
- 采用CNN-LSTM级联网络解决光学逆设计中的多解映射难题
- 该组合在准确率与训练速度间取得最佳平衡，优于其他八种配置
- 适合需要高效精准设计光学薄膜的科研与工业应用
薄膜的光学特性高度依赖各层厚度。准确预测厚度及其对应的光学性能，在多层薄膜的光学逆向设计中至关重要。传统方法需大量数值模拟与优化，耗时较长。本文利用深度学习实现SiO2/TiO2多层薄膜透射谱的逆向设计，提出一种级联神经网络（TNN），可有效缓解深度学习逆向设计中常见的“一对多”映射问题。TNN由反向神经网络与预训练正向神经网络串联构成，均基于多层感知机（MLP）算法。本文进一步探索在TNN中引入卷积神经网络（CNN）或长短期记忆网络（LSTM）的可行性。结果表明，基于LSTM-LSTM的TNN精度最高但训练时间最长；而基于CNN-LSTM的TNN在准确率与速度之间达到最优平衡，是理想选择。
原文摘要 · Abstract (English)
Optical properties of thin film are greatly influenced by the thickness of each layer. Accurately predicting these thicknesses and their corresponding optical properties is important in the optical inverse design of thin films. However, traditional inverse design methods usually demand extensive numerical simulations and optimization procedures, which are time-consuming. In this paper, we utilize deep learning for the inverse design of the transmission spectra of SiO2/TiO2 multilayer thin films. We implement a tandem neural network (TNN), which can solve the one-to-many mapping problem that greatly degrades the performance of deep-learning-based inverse designs. In general, the TNN has been implemented by a back-to-back connection of an inverse neural network and a pre-trained forward neural network, both of which have been implemented based on multilayer perceptron (MLP) algorithms. In this paper, we propose to use not only MLP, but also convolutional neural network (CNN) or long short-term memory (LSTM) algorithms in the configuration of the TNN. We show that an LSTM-LSTM-based TNN yields the highest accuracy but takes the longest training time among nine configurations of TNNs. We also find that a CNN-LSTM-based TNN will be an optimal solution in terms of accuracy and speed because it could integrate the strengths of the CNN and LSTM algorithms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。