Authors: Daniel G. Stroppa | Roberto dos Reis
Source:  (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Contrast Optimization Aided by Machine Learning Applied to Virtual 4D-STEM Images <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d15100e83">Scanning Transmission Electron Microscopy (STEM) has revolutionized imaging due to its high spatial resolution and easy interpretation due to varied contrast mechanisms. The enhanced flexibility in terms of scattering range detection in comparison with STEM is one of the reasons that led to the increased interest in 4D-STEM [ <a class="xref-link" href="#r1">1</a>]. Supported by the recent evolution of pixelated detectors, 4D-STEM currently allows for the recording of the complete electron scattering range at speeds commensurate with traditional STEM experiments [ <a class="xref-link" href="#r2">2</a>]. With the possibility of flexible reconstruction of virtual STEM images with arbitrary detector shapes, contrast optimization for sample regions with different scattering cross-sections is envisioned. This study focus on the optimization of contrast in virtual 4D-STEM images, employing both user-guided and machine-learning (ML) optimization approaches. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d15100e91">Reference samples from semiconductor devices and supported catalysts were measured with fast 4D-STEM under experimental conditions mirroring standard STEM imaging practices, with 1024x1024 scan positions and 10 us dwell time. The resultant datasets comprised 10 <sup>6</sup> diffraction patterns with 96x96 pixels each, presenting a great challenge for manual contrast optimization due to the vast data volumes and nuanced contrast differences within the areas of the specimens. <a class="xref-link" href="#fg001">Figure 1</a> shows manual contrast optimization applied to virtual 4D-STEM images, serving as a reference for our machine learning (ML)-aided methodology. In its right panel, a line profile from different virtual images is presented with indications of contrast levels, offering a quantitative perspective on the enhancements achieved through manual methods. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d15100e99">In this current study, we develop a computational framework designed to automate the enhancement of contrast in similar regions within 4D-STEM data. <div class="fig panel" id="fg001"> <a class="named-anchor" id="fg001"> <!-- named anchor --> </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/68bd80f6-764f-4709-9487-385daba3e61c/ScienceOpen/image/387_6724b693467bf-DanielStroppaAPMC2025_PS03_contrastAI_fig1.jpg"/> </div> <div class="panel-content"> <div class="label">Fig. 1.</div> <div class="caption" id="d15100e104"> <p class="first" dir="auto" id="d15100e105">(left) Virtual BF STEM image, inset (upper left) indicates an example scattering and with the virtual mask overlay. (center) Virtual STEM image collected with an optimized annular mask. (right) Line profile from virtual images with the relative contrast level calculated from normalized intensities, with an An contrast increase from 28% to 39% is observed between images reconstructed from virtual BF and with an optimized annular mask. </div> </div> </div> Contrast (vision) ; Computer science ; Artificial intelligence ; Computer vision ; Machine learning ; Pattern recognition (psychology) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications