Automated extraction of critical dimension from SEM with Weave

Current best practices for the extraction of critical dimensions (CDs) from microscopic images requires semiconductor process engineers to analyze images one by one, which is tedious, prone to human bias, time-consuming and expensive. Automated CD extraction using machine learning methodologies is an approach to accelerate and improve the accuracy of this process. Deep learning convolutional neural nets specifically can be used effectively for image segmentation and identification of different material regions; however, providing enough annotated data for training and testing is an ongoing challenge. Here, we demonstrate a method where only one sample image is needed for the neural net to learn how to extract the CDs of interests. The methodology is specifically demonstrated for extracting CDs from a metal assisted chemical etching process. Each experimental SEM image is automatically measured in about 45 seconds. The extracted CD measurements are within 4 nm (<5% error) of the human measured CDs. This automated extraction enables process engineers to improve the accuracy of their metrology workflow, reduce their total metrology costs, and accelerate their process development.