Method to Estimate Dislocation Densities from Images of α ‐Ga 2 O 3 ‐Based Corundum Oxides Using the Computer Vision YOLO Algorithm

• Published in: physica status solidi (b) Vol. 262; no. 8
• Main Authors: Dang, Giang T., Kawaharamura, Toshiyuki, Allen, Martin W.
• Format: Journal Article
• Language: English
• Published: 01.08.2025
• ISSN: 0370-1972; 1521-3951
• DOI: 10.1002/pssb.202400439

This work applies the computer vision “You only look once” (YOLO) algorithm to extract bounding boxes around dislocations in weak‐beam dark‐field transmission electron microscopy (WBDF TEM) images of semiconductor thin films. A formula is derived to relate the sum of the relative heights of the bounding boxes to the dislocation densities in the films. WBDF TEM images reported in the literature and taken from our α‐Ga 2 O 3 samples are divided into train, evaluation, and test datasets. Different models are trained using the train dataset and evaluated using the evaluation dataset to find the best confidence values, which are used to select the best model based on the performance against the test data set. For α‐Ga 2 O 3 thin films, dislocation density output by this model is on average ≈58% of those estimated by the traditional Ham method. A factor of 4/π may contribute to the systematic underestimation of the model versus the Ham method.