论文标题
设计空间重新聚集体在反设计的纳米光器设备中强制执行硬几何约束
Design space reparameterization enforces hard geometric constraints in inverse-designed nanophotonic devices
论文作者
论文摘要
逆设计算法是实现高性能，自由形式的纳米光子设备的基础。当前执行几何约束的方法，例如实际制造约束，是启发式且不强大的。在这项工作中，我们表明，可以通过重新参与设计空间本身来对逆设计的设备施加硬性几何约束。候选设备的布局不是在物理设备空间中评估和修改设备，而是在无约束潜在空间中定义，并在数学上转换为物理设备空间，该空间可牢固地施加几何约束。使用反向传播对其潜在空间表示形式进行了对物理设备的修改。作为概念验证的演示，我们应用重新聚集化以在本地和全球拓扑优化仪中对Metagratings进行严格的最小特征大小限制。我们预计，重新聚体化的概念将为一个一般而有意义的平台提供将物理和物理约束纳入任何基于梯度的优化器，包括机器学习支持的全球优化器。
Inverse design algorithms are the basis for realizing high-performance, freeform nanophotonic devices. Current methods to enforce geometric constraints, such as practical fabrication constraints, are heuristic and not robust. In this work, we show that hard geometric constraints can be imposed on inverse-designed devices by reparameterizing the design space itself. Instead of evaluating and modifying devices in the physical device space, candidate device layouts are defined in a constraint-free latent space and mathematically transformed to the physical device space, which robustly imposes geometric constraints. Modifications to the physical devices, specified by inverse design algorithms, are made to their latent space representations using backpropagation. As a proof-of-concept demonstration, we apply reparameterization to enforce strict minimum feature size constraints in local and global topology optimizers for metagratings. We anticipate that concepts in reparameterization will provide a general and meaningful platform to incorporate physics and physical constraints in any gradient-based optimizer, including machine learning-enabled global optimizers.
