Inverse design and flexible parameterization of meta-optics using algorithmic differentiation
Inverse design and flexible parameterization of meta-optics using algorithmic differentiation
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DOI：
10.1038/s42005-021-00568-6
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发表时间：
2021-03-31
影响因子：
5.5
通讯作者：
Majumdar, Arka
中科院分区：
文献类型：
--
作者：
Colburn, Shane;Majumdar, Arka
超薄超光学器件提供无与伦比的多功能光线控制。然而，下一代光学技术需要前所未有的性能。这可能需要设计超越人类直觉能力的算法。对于伴随方法，这需要明确地导出梯度，这有时对于某些光子学问题是具有挑战性的。现有的技术还包括一个拼凑的应用程序特定的算法，每一个集中在范围和散射体类型。在这里，我们利用人工神经网络中使用的算法微分，将光子设计参数视为可训练的权重，光源作为输入，并将器件性能封装在损失函数中。通过解决一个复杂的，退化的本征问题和制定严格的耦合波分析作为一个计算图，我们支持任意的，参数化的散射体和拓扑优化。随着迭代次数低于成本的两个典型的伴随方法的前向模拟，我们产生多层，多功能，和非周期性的元光学。作为一个开源平台，我们可以适应其他算法和问题，实现快速灵活的元光学设计。元光学和光子器件的设计提出了许多高维，制造约束，计算昂贵的优化问题。在这里，作者提出了一个基于算法微分的框架，以优化具有任何形状或拓扑结构的任意元光学器件，从而实现光子学和复杂元光学器件的快速逆向设计。
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.The design of meta-optics and photonics components poses a number of high-dimensional, fabrication-constrained, computationally expensive optimization problems. Here, the authors propose a framework based on algorithmic differentiation to optimize arbitrary meta-optical devices with any shape or topology, enabling rapid inverse design of photonics and complex meta-optical devices.