论文标题
使用绿色功能形式主义的逐像素纳米光量优化大量平行的像素
Massively parallel pixel-by-pixel nanophotonic optimization using a Green's function formalism
论文作者
论文摘要
我们介绍了一种有效的并行化方案，以使用基于绿色的函数形式主义实现像素纳米光子优化。我们提案中的关键见解是将优化算法作为大规模数据处理管道的重新标记，这允许在数千名工人中有效分配计算任务。我们通过行使实施的实用性来证明我们实施的实用性，以优化问题大小的高数值孔径聚焦，否则这对于基于绿色的函数方法而言是遥不可及的。最后，我们强调了与强大的想法的联系，从增强学习是一种自然的推论，即重新诠释纳米光子逆设计问题作为由像素逐像素优化范式启用的图形遍历。
We introduce an efficient parallelization scheme to implement pixel-by-pixel nanophotonic optimization using a Green's function based formalism. The crucial insight in our proposal is the reframing of the optimization algorithm as a large-scale data processing pipeline, which allows for the efficient distribution of computational tasks across thousands of workers. We demonstrate the utility of our implementation by exercising it to optimize a high numerical aperture focusing metalens at problem sizes that would otherwise be far out of reach for the Green's function based method. Finally, we highlight the connection to powerful ideas from reinforcement learning as a natural corollary of reinterpreting the nanophotonic inverse design problem as a graph traversal enabled by the pixel-by-pixel optimization paradigm.
