A New SCAN Method for Unsupervised Learning: Developing an Auto-Labeling Framework in Semiconductor Manufacturing

This paper proposes a novel self-supervised clustering framework for automatic defect annotation in semiconductor manufacturing, aiming to reduce the heavy reliance on expert-labeled data. Building upon the Semantic Clustering by Adopting Nearest-neighbors (SCAN) model, we introduce a new SCAN methodology that integrates three key innovations: a redesigned pretext task, an enhanced SCAN model with neighbor loss, and a self-labeling mechanism. The new pretext task replaces the traditional ResNet18 backbone with MobileNetV2 and incorporates focal loss to address class imbalance, enabling more effective feature extraction from unlabeled wafer maps. The SCAN model is further refined through a neighbor loss function that leverages high-confidence samples to iteratively correct pseudo-labels, improving clustering accuracy. Additionally, we apply strong augmentation techniques and a confidence-based thresholding strategy to enhance model robustness. Experimental evaluations on the WM-811K and MixedWM38 datasets demonstrate that the proposed unsupervised clustering framework achieves a notable accuracy improvement (from 36.5% to 57.7% and 36.09% to 57.69%, respectively) compared to the original SCAN, with further gains up to 66.20% and 64.80% using high-confidence sample filtering. The results confirm that our approach significantly enhances classification performance in unsupervised settings and offers a practical solution for scalable, automated defect labeling in intelligent manufacturing environments.