Application of a novel local and automatic PCA algorithm for diffraction pattern denoising in TEM-ASTAR analysis in microelectronics

• Published in: Ultramicroscopy Vol. 267; p. 114059
• Main Authors: Printemps, Tony, Dabertrand, Karen, Vives, Jérémy, Valery, Alexia
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2024
• Subjects: ACOM-TEM; ASTAR; Denoising; Diffraction pattern; Marchenko-Pastur; PCA; ACOM-TEM; Marchenko-Pastur; ASTAR; Diffraction pattern; Denoising; PCA
• ISSN: 0304-3991; 1879-2723; 1879-2723
• DOI: 10.1016/j.ultramic.2024.114059

•PCA denoising of TEM-ASTAR dataset improves post-treatment analysis.•Automatic thresholding of PCA using theoretical noise matrix's spectrum.•Locality of the algorithm aligned to localized nature of crystallographic grains.•Reduction of data acquisition times by a factor of five maintaining analytical results. This paper introduces a novel denoising method for TEM-ASTAR™ Diffraction Pattern (DP) datasets, termed LAT–PCA (Local Automatic Thresholding – Principal Component Analysis). This approach enhances the established PCA algorithm by partitioning the 4D dataset (a 2D map of 2D DPs) into localized windows. Within these windows, PCA identifies a basis where the physical signal predominantly resides in the higher-order principal components. By thresholding lower-order components, the method effectively reduces noise while retaining the essential features of the DPs. The locality of the approach, focusing on small windows, enhances computational efficiency and aligns with the localized nature of the crystallographic grain signals in ASTAR. The automatic aspect of the method employs a theoretical pure noise distribution, i.e. a Marchenko-Pastur Distribution, to set a threshold, beyond which the components are mostly noise. The LAT–PCA method offers significant reductions in acquisition and post-processing times. With denoised data, selecting the correct parameters for accurate phase maps and grain orientations becomes more straightforward, facilitating robust quantitative grain analysis. Experiments performed on a silicon-germanium-carbon sample validate the method's efficacy. The sample was analyzed with varying acquisition times to produce a high signal-to-noise ratio reference dataset and a lower ratio test dataset. The LAT–PCA algorithm's denoising results on the test dataset were benchmarked against the reference, demonstrating considerable improvements and reliability. In summary, LAT–PCA is an effective, automatic solution for denoising TEM DP datasets. Its adaptability to different noise levels and local processing capability makes it a valuable tool for enhancing dataset quality and reducing the time required for data acquisition and analysis. This method can minimize acquisition time, conserve microscope usage, and reduce sample drift and deterioration, leading to more accurate characterizations with fewer deformation artifacts.