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 constraints. This work paves the way for an efficient characterization of structured samples at the sub-wavelength scale.

中文翻译：

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优化照明以在相干衍射成像中进行精确的多参数估计

相干衍射成像（CDI）被广泛用于根据衍射强度模式的测量来表征结构化样品。我们引入了一个数值框架来量化从测量数据估计表征样品特征的任何给定参数集时可以达到的精度。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库优化Fisher信息度量，我们演示了如何确定在指定实验约束下使估计误差最小的最佳照明方案。这项工作为在亚波长范围内有效表征结构化样品铺平了道路。