论文标题
使用算法分化对元访问的逆设计和柔性参数化
Inverse design and flexible parameterization of meta-optics using algorithmic differentiation
论文作者
论文摘要
Ultrathin Meta-Optics提供了无与伦比的多功能控制。但是，下一代光学技术需要前所未有的性能。这可能需要超过人类直觉能力的设计算法。对于伴随方法，这需要明确得出梯度，这有时对于某些光子学问题而言是具有挑战性的。现有技术还包括一个特定于应用程序的算法的拼布，每种算法都集中在范围和散射器类型上。在这里，我们利用人工神经网络中使用的算法分化，将光子设计参数视为可训练的权重，光源作为输入，并将设备性能封装在损耗函数中。通过求解复杂的，退化的本本概况并将严格的耦合波分析作为计算图，我们支持任意的，参数化的散射器和拓扑优化。由于迭代时间低于典型的伴随方法的成本，我们生成了多层，多功能元元元观点。作为适合其他算法和问题的开源平台，我们启用了快速，灵活的元元设计。
Ultrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical technologies, however, demand unprecedented performance. This will likely require design algorithms surpassing the capability of human intuition. For the adjoint method, this requires explicitly deriving gradients, which is sometimes challenging for certain photonics problems. Existing techniques also comprise a patchwork of application-specific algorithms, each focused in scope and scatterer type. Here, we leverage algorithmic differentiation as used in artificial neural networks, treating photonic design parameters as trainable weights, optical sources as inputs, and encapsulating device performance in the loss function. By solving a complex, degenerate eigenproblem and formulating rigorous coupled-wave analysis as a computational graph, we support both arbitrary, parameterized scatterers and topology optimization. With iteration times below the cost of two forward simulations typical of adjoint methods, we generate multilayer, multifunctional, and aperiodic meta-optics. As an open-source platform adaptable to other algorithms and problems, we enable fast and flexible meta-optical design.
