Subpixel correction of diffraction pattern shifts in ptychography via automatic differentiation

TL;DR

This paper introduces an automatic differentiation-based method to correct subpixel shifts in diffraction patterns during ptychography, improving reconstruction accuracy and automation without manual tuning.

Contribution

The novel approach integrates shift correction into the reconstruction process using automatic differentiation, enabling simultaneous refinement of object, probe, and shift parameters.

Findings

Achieves subpixel correction with deviations below 0.5 pixels in simulations.

Demonstrates robustness and effectiveness in EUV experimental data.

Enhances automation and reduces manual intervention in ptychography.

Abstract

Ptychography, a coherent diffraction imaging technique, has become an indispensable tool in materials characterization, biological imaging, and nanostructure analysis due to its capability for high-resolution, lensless reconstruction of complex-valued images. In typical workflows, raw diffraction patterns are commonly cropped to isolate the valid central region before reconstruction. However, if the crop is misaligned from the diffraction pattern's zero-order, reconstruction may suffer from slower convergence, phase wrapping, and reduced image fidelity. These issues are further exacerbated in experimental configurations involving reflective geometries or broadband illumination, where incorrect cropping introduces systematic preprocessing errors that compromise the entire ptychographic inversion. To address this challenge, we present an approach based on automatic differentiation (AD), where the cropping shift is treated as an optimizable parameter within the reconstruction framework. By integrating shift correction into the backpropagation loop, our method simultaneously refines the object, probe, and shift positions without requiring manual tuning. Simulation results demonstrate that, even with initial offsets ranging up to 5 pixels, the proposed method achieves subpixel correction, with an average deviation below 0.5 pixels. Experiments in the extreme ultraviolet (EUV) regime further validate the method's robustness and effectiveness. This AD-based strategy enhances the automation and robustness of ptychographic reconstructions, and is adaptable to diverse experimental conditions.