Automated Dislocation Detection in Electron Channelling Contrast Imaging: A Comparative Study of Rule-Based, Neural Network, and Deep Learning Approaches
Quantifying threading dislocations in semiconductor materials via electron channelling contrast imaging (ECCI) is heavily bottlenecked by slow manual analysis. This work benchmarks three automated detection pipelines on ECCI micrographs of gallium nitride (GaN) against a statistical ground truth. A classical rule-based computer vision approach proved unreliable due to extensive per-image tuning requirements, while a convolutional neural network (CNN)-based multi-stage classification and locator method achieved 87% accuracy but required significant time for a test image and was less efficient in high-density regions. By contrast, a unified single-stage you only look once (YOLOv8) architecture achieved a counting accuracy of 98.6%, alongside 98.7% precision and 98.7% recall across 7451 dislocations over multiple images, with rapid inference times. Successful deployment of YOLOv8 required addressing two domain-specific machine learning challenges. First, to mitigate the model's scale sensitivity, an adaptive gaussian tile-sizing algorithm was developed to optimise the field of view per image. Second, the deployed confidence threshold (0.025) differed substantially from the suggested inference default value for YOLOv8 (0.25), raising counting accuracy from 90.8% to 99.3% on a subsection of the benchmark image. In this case, low confidence scores represented physical signal strength rather than classification ambiguity. This unified, scale-adaptive approach demonstrates practical viability for high-throughput, quantitative semiconductor defect characterisation.