Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer

TL;DR

This paper presents an automated microscopy image analysis pipeline that combines unsupervised and supervised learning techniques to detect and classify defects across an entire 4H-SiC wafer, significantly improving efficiency.

Contribution

It introduces a novel integrated approach that automates defect detection and classification in large microscopy datasets, enabling comprehensive wafer analysis.

Findings

Successfully processed 40,000 images for defect analysis

Achieved high accuracy in defect classification

Enabled full-wafer defect mapping

Abstract

Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms as well as tailoring the production processes. Analysis of microscopy images that reveal defects typically requires image analysis tasks such as segmentation and object detection. With the permanently increasing amount of data that is produced by experiments, handling these tasks manually becomes more and more impossible. In this work, we combine various image analysis and data mining techniques for creating a robust and accurate, automated image analysis pipeline. This allows for extracting the type and position of all defects in a microscopy image of a KOH-etched 4H-SiC wafer that was stitched together from approximately 40,000 individual images.