Lightweight Model for Spatial Pattern Classification in Electrical Wafer Maps with Interactive Human Labeling

In semiconductor manufacturing, spatial pattern recognition is essential for identifying defects or obtaining other crucial information on electrical wafer maps. During the wafer testing stage, deep learning methods are widely used for their powerful feature extraction capabilities. The aim of this thesis is to propose a lightweight CNN model that achieves comparable or superior results to more complex models, enabling faster training and greater flexibility for experimentation and futurue improvements. Additionally, we introduce a human-in-the-loop process, where domain experts label recognized spatial patterns. By retraining the model with these human-labeled images, our objective is to enhance classification accuracy and optimize defect detection.