Optimizing illumination for precise multi-parameter estimations in coherent diffractive imaging

TL;DR

This paper introduces a numerical framework based on Fisher information to benchmark and optimize illumination schemes in coherent diffractive imaging, enhancing precision in multi-parameter sample characterization at sub-wavelength scales.

Contribution

It presents a novel Fisher information-based method to evaluate and optimize illumination in CDI, improving parameter estimation accuracy.

Findings

Optimized illumination schemes reduce estimation errors.

Fisher information provides a benchmark for CDI performance.

Deep learning accelerates the optimization process.

Abstract

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.