The simulation of nanophotonic structures relies on electromagnetic solvers,
which play a crucial role in understanding their behavior. However, these
solvers often come with a significant computational cost, making their
application in design tasks, such as optimization, impractical. To address this
challenge, machine learning techniques have been explored for accurate and
efficient modeling and design of photonic devices. Deep neural networks, in
particular, have gained considerable attention in this field. They can be used
to create both forward and inverse models. An inverse modeling approach avoids
the need for coupling a forward model with an optimizer and directly performs
the prediction of the optimal design parameters values.
In this paper, we propose an inverse modeling method for nanophotonic
structures, based on a mixture density network model enhanced by transfer
learning. Mixture density networks can predict multiple possible solutions at a
time including their respective importance as Gaussian distributions. However,
multiple challenges exist for mixture density network models. An important
challenge is that an upper bound on the number of possible simultaneous
solutions needs to be specified in advance. Also, another challenge is that the
model parameters must be jointly optimized, which can result computationally
expensive. Moreover, optimizing all parameters simultaneously can be
numerically unstable and can lead to degenerate predictions. The proposed
approach allows overcoming these limitations using transfer learning-based
techniques, while preserving a high accuracy in the prediction capability of
the design solutions given an optical response as an input. A dimensionality
reduction step is also explored. Numerical results validate the proposed
method.

本文提出了一种基于混合密度网络模型和迁移学习增强的逆向建模方法，用于纳米光子结构，该方法在输入光学响应的情况下能够准确预测设计解决方案的能力，并克服了混合密度网络模型中的多个挑战。

基于混合密度网络的纳米光子学中的迁移学习辅助逆向建模

Transfer learning-assisted inverse modeling in nanophotonics based on  mixture density networks

Nanophotonic structures have versatile applications including solar cells,
anti-reflective coatings, electromagnetic interference shielding, optical
filters, and light emitting diodes. To design and understand these nanophotonic
structures, electrodynamic simulations are essential. These simulations enable
us to model electromagnetic fields over time and calculate optical properties.
In this work, we introduce frameworks and benchmarks to evaluate nanophotonic
structures in the context of parametric structure design problems. The
benchmarks are instrumental in assessing the performance of optimization
algorithms and identifying an optimal structure based on target optical
properties. Moreover, we explore the impact of varying grid sizes in
electrodynamic simulations, shedding light on how evaluation fidelity can be
strategically leveraged in enhancing structure designs.

本研究介绍了应用于纳米光子结构的电动力学模拟和基准测试的框架和标杆，以评估结构的性能和优化算法的效果，并探索了电动力学模拟中网格尺寸变化对结构设计的影响。

纳米光子结构和参数设计模拟的数据集和基准

Datasets and Benchmarks for Nanophotonic Structure and Parametric Design  Simulations

The parallelism of optics and the miniaturization of optical components using
nanophotonic structures, such as metasurfaces present a compelling alternative
to electronic implementations of convolutional neural networks. The lack of a
low-power optical nonlinearity, however, requires slow and energy-inefficient
conversions between the electronic and optical domains. Here, we design an
architecture which utilizes a single electrical to optical conversion by
designing a free-space optical frontend unit that implements the linear
operations of the first layer with the subsequent layers realized
electronically. Speed and power analysis of the architecture indicates that the
hybrid photonic-electronic architecture outperforms sole electronic
architecture for large image sizes and kernels. Benchmarking of the
photonic-electronic architecture on a modified version of AlexNet achieves a
classification accuracy of 87% on images from the Kaggle Cats and Dogs
challenge database.

利用自由空间光学前端单元实现第一层的线性操作，然后通过电子方式实现后续层的混合光电架构，在 Kaggle 猫狗挑战数据库中获得了 87% 的图像分类精度。