Interactive image annotation and AI-assisted segmentation of TEM images for automatic CD measurement

As the semiconductor structures become increasingly miniaturized and complex, the process of measuring and analyzing the structure using a microscope becomes crucial. High-resolution transmission electron microscope (TEM) images are widely used, but they are expensive to acquire and analyze. If the region and boundary of the material in the TEM image can be automatically segmented, the measurement cost will be reduced. We proposed a method to generate a segmentation label for a TEM image using a deep learning model that performs segmentation based on weak supervision and active learning. The proposed method achieved an accuracy of 98% in 10% of the time compared to the manual method. This approach will reduce the cost of high-resolution TEM image analysis and accelerate the semiconductor device development process.