标题： 基于扩展神经伴随和回归激活映射的可解释性光学多层薄膜逆向设计 显示英文标题

标题： Interpretable inverse design of optical multilayer thin films based on extended neural adjoint and regression activation mapping

摘要： 我们提出了一种扩展的神经伴随（ENA）框架，该框架满足人工智能辅助光学多层薄膜（OMTs）逆向设计的六个关键标准：准确性、效率、多样性、可扩展性、灵活性和可解释性。 为了增强现有神经伴随方法的可扩展性，我们提出了一种针对OMTs的新前向神经网络架构，并将材料损失函数引入现有的神经伴随损失函数中，有助于探索OMTs的材料配置。 此外，我们详细阐述了所提出的前向神经网络架构的回归激活映射（F-RAM），这是一种旨在提高可解释性的特征可视化方法。 我们通过进行消融研究验证了材料损失的有效性，其中系统地移除损失函数的每个组件并进行评估。 结果表明，包含材料损失显著提高了准确性和多样性。 为了证实基于ENA的逆向设计性能，我们将它与基于残差网络的全局优化网络（Res-GLOnet）进行了比较。 与Res-GLOnet相比，ENA在逆向设计中产生了更高准确性和更好多样性的OMT解决方案。 为了展示可解释性，我们将F-RAM应用于通过所提出的ENA方法获得的具有相似光学特性的多种OMT结构。 我们展示了不同OMT结构的特征重要性分布在表现出类似光学特性的情况下是一致的，尽管材料配置、层数和厚度存在差异。 此外，我们通过将OMTs的初始层限制为SiO2和100 nm来展示ENA方法的灵活性。 显示英文摘要

摘要： We propose an extended neural adjoint (ENA) framework, which meets six key criteria for artificial intelligence-assisted inverse design of optical multilayer thin films (OMTs): accuracy, efficiency, diversity, scalability, flexibility, and interpretability. To enhance the scalability of the existing neural adjoint method, we present a novel forward neural network architecture for OMTs and introduce a material loss function into the existing neural adjoint loss function, facilitating the exploration of material configurations of OMTs. Furthermore, we present the detailed formulation of the regression activation mapping for the presented forward neural network architecture (F-RAM), a feature visualization method aimed at improving interpretability. We validated the efficacy of the material loss by conducting an ablation study, where each component of the loss function is systematically removed and evaluated. The results indicated that the inclusion of the material loss significantly improves accuracy and diversity. To substantiate the performance of the ENA-based inverse design, we compared it against the residual network-based global optimization network (Res-GLOnet). The ENA yielded the OMT solutions of an inverse design with higher accuracy and better diversity compared to the Res-GLOnet. To demonstrate the interpretability, we applied F-RAM to diverse OMT structures with similar optical properties, obtained by the proposed ENA method. We showed that distributions of feature importance for various OMT structures exhibiting analogous optical properties are consistent, despite variations in material configurations, layer number, and thicknesses. Furthermore, we demonstrate the flexibility of the ENA method by restricting the initial layer of OMTs to SiO2 and 100 nm.