论文标题
优化相干衍射成像中精确的多参数估计的照明
Optimizing illumination for precise multi-parameter estimations in coherent diffractive imaging
论文作者
论文摘要
连贯的衍射成像（CDI）广泛用于表征从衍射强度模式的测量中的结构化样品。我们引入了一个数值框架，以量化在估计从测量数据中表征样本的任何给定参数集时，可以实现的精度。该方法基于Fisher信息矩阵的计算，为评估CDI方法的性能提供了明确的基准。此外，通过使用深度学习优化文库优化Fisher信息指标，我们演示了如何确定最佳的照明方案，该方案将指定的实验约束下的估计误差最小化。这项工作为以下尺度上有效地表征结构化样品铺平了道路。
Coherent diffractive imaging (CDI) is widely used to characterize structured samples from measurements of diffracting intensity patterns. We introduce a numerical framework to quantify the precision that can be achieved when estimating any given set of parameters characterizing the sample from measured data. The approach, based on the calculation of the Fisher information matrix, provides a clear benchmark to assess the performance of CDI methods. Moreover, by optimizing the Fisher information metric using deep learning optimization libraries, we demonstrate how to identify the optimal illumination scheme that minimizes the estimation error under specified experimental constrains. This work paves the way for an efficient characterization of structured samples at the sub-wavelength scale.
