Defect contrast with 4D-STEM: Understanding crystalline order with virtual detectors and beam modification
Stephanie M. Ribet, Colin Ophus, Roberto dos Reis, Vinayak P. Dravid

TL;DR
This paper explores advanced virtual detector techniques in 4D-STEM to improve defect detection and material characterization at atomic scales, using simulations and experimental data to demonstrate enhanced contrast methods.
Contribution
It introduces novel virtual detector configurations and beam modifications that enhance defect contrast and local order detection in 4D-STEM imaging.
Findings
Small convergence angles with rotationally varying detectors optimize defect signals.
Experimental graphene data confirms simulation predictions for defect contrast enhancement.
Beam phase and amplitude modifications can tailor contrast for specific material features.
Abstract
Material properties strongly depend on the nature and concentration of defects. Characterizing these features may require nano- to atomic-scale resolution to establish structure-property relationships. 4D-STEM, a technique where diffraction patterns are acquired at a grid of points on the sample, provides a versatile method for highlighting defects. Computational analysis of the diffraction patterns with virtual detectors produces images that can map material properties. Here, using multislice simulations, we explore different virtual detectors that can be applied to the diffraction patterns that go beyond the binary response functions that are possible using ordinary STEM detectors. Using graphene and lead titanate as model systems, we investigate the application of virtual detectors to study local order and in particular defects. We find that using a small convergence angle with a…
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Taxonomy
TopicsAdvanced Surface Polishing Techniques · Electron and X-Ray Spectroscopy Techniques · Integrated Circuits and Semiconductor Failure Analysis
