Reflection ptychography Siemens star soft X-ray dataset

Guenzing, Damian 1 2 ; Sasaki, Dayne 1 2 3 ; Ditter, Alexander1 2; Levitan, Abraham4; Gullikson, Eric1; Dhuey, Scott1 5; Gashi, Arian1 5; Ohldag, Hendrik1 2; Roy, Sujoy1 2; Shapiro, David1 2; Comin, Riccardo3; Morley, Sophie1 2

Published Mar 14, 2026 on Dryad. https://doi.org/10.5061/dryad.wpzgmsc39

Data files

Mar 14, 2026 version files 2.46 GB

anisotropic_blur_simulation.ipynb

483.03 KB
README.md

3.37 KB
reflection_ptychography_dataset.stxm

2.46 GB
resolution_test_pattern.GDS

10.15 KB
siemens_reconstruction_publish.ipynb

2.43 MB
test_pattern_sem.tif

3.15 MB

Abstract

The dataset contains raw data and Jupyter notebooks associated with our research on reflection-geometry soft X-ray ptychography, as presented in our paper. Through this imaging mode, we address the limitations of traditional transmission geometries, which restrict the analysis of thicker or bulk samples in the soft X-ray range. Our dataset includes high-resolution images obtained from a lithographically defined Siemens star and barcode test pattern on a multilayer substrate, demonstrating a full-pitch resolution of ~45 nanometers confirmed through Fourier ring correlation analysis. Additionally, this dataset contains the layout file and Jupyter notebook used to simulate the anisotropic blurring of our tilted resolution test sample as described in our paper. This dataset provides a foundational resource for researchers interested in nondestructive X-ray imaging techniques and extends the capabilities of soft X-ray studies beyond the constraints of transmissive sample preparation.

Dataset DOI: 10.5061/dryad.wpzgmsc39

Description of the data and file structure

Overview

This repository contains the dataset associated with our research on reflection-geometry soft X-ray ptychography. The dataset includes raw imaging data and the reconstruction code used to analyze the data, the layout data for producing the patterned structure, and code to generate simulations of the anisotropic blurring observed in our work, facilitating further research and validation of our findings.

Files

siemens_reconstruction_publish.ipynb: This Jupyter notebook contains the reconstruction code used for processing the raw data. The code provides step-by-step instructions for reconstructing images from the collected ptychographic raw data contained within reflection_ptychography_dataset.stxm.
reflection_ptychography_dataset.stxm: This file contains the raw data collected from the soft X-ray ptychography experiments. The dataset includes images obtained from a lithographically defined Siemens star and barcode test pattern on a multilayer substrate. This file is an HDF5 file whose extension has been renamed to '.stxm'. This file can be directly opened either using a GUI-based HDF5 reader (e.g., HDFView, https://www.hdfgroup.org/download-hdfview/) after renaming the extension to '.h5', or through Python using the h5py package (https://www.h5py.org/). Installing the CDTools Python package for running siemens_reconstruction_publish.ipynb (see requirements) will also install h5py. Within that Jupyter Notebook, there will be a block of code that will open this '.stxm' file.
test_pattern_sem.tif: This file contains the SEM image of the studied Siemens star and barcode test pattern, measured with a Zeiss Ultra 60 SEM.
resolution_test_pattern.GDS: This file contains the layout file used to generate the Siemens star and barcode test patterns.
anisotropic_blur_simulation.ipynb: This Jupyter notebook contains the code used to process the layout file (resolution_test_pattern.GDS) and simulate aspects of the anisotropic blurring effect observed in our work. This code provides step-by-step instructions on generating the simulation.

Usage

To perform reconstructions using this dataset, follow these steps:

Download the provided files.
Install the required packages (see requirements)
Open the siemens_reconstruction_publish.ipynb notebook in a Jupyter environment.
Load the reflection_ptychography_dataset.stxm file within the notebook and execute the code cells to perform the reconstruction of the imaging data.

To run the anisotropic blurring simulations, follow these steps:

Download the provided files.
Install the required packages (see requirements)
Open the anisotropic_blur_simulation.ipynb notebook in a Jupyter environment.
Load the resolution_test_pattern.GDS file within the notebook and execute the code cells to perform the simulation.

Requirements

cdtools

klayout

Citation

If you use this dataset in your research, please cite our corresponding paper or this dataset.