Event-sparse stack denoising for 4D-STEM applications

TL;DR

This paper presents a novel denoising method for 4D-STEM data that enhances image quality and defect detection sensitivity by processing locally time-resolved electron event-sparse datasets, enabling lower dose imaging and material degradation analysis.

Contribution

It introduces a new denoising approach for 4D-STEM that incorporates an extra time dimension and compares two sparsity-based pipelines, improving image quality and defect detection capabilities.

Findings

Denoised data achieves similar PSNR to raw data at 16% exposure time.

Denoising increases defect detection sensitivity by 4.1 times.

LTR-STEM enables material degradation tracking and dose estimation.

Abstract

We introduce a denoising method for four-dimensional scanning transmission electron microscopy (4D-STEM) that relies on processing local, scan position-independent electron event-sparse data stacks, called event-sparse stack denoising. This method adds an extra time dimension during data collection by recording multiple electron event-sparse diffraction patterns. The resulting datasets are effectively five-dimensional, referred to as locally time-resolved STEM (LTR-STEM). Diffraction data stacks at each scan position are processed using one of two sparsity denoising pipelines: 1) the density-based spatial clustering of applications with noise (DBSCAN) algorithm followed by multi-step persistence thresholding, or 2) sparse principal component analysis (sparse PCA), followed by single-step thresholding. Both methods perform well for diffraction data denoising, as shown by simulated peak signal-to-noise ratio (PSNR) curves, denoised experimental data for virtual imaging, and application-specific denoising for defect detection. PSNR analysis indicates that sparsity-denoised 4D-STEM data reaches the same PSNR as raw data at approximately 16% of the exposure time, demonstrating comparable image quality with a lower dose. In defect detection, a 4.1x increase in sensitivity to relative radial disk shift is observed in the denoised data. Moreover, the LTR-STEM technique may be used to inspect material degradation by tracking changes in diffraction disk intensity, allowing for critical dose estimation and exposure-selective imaging.