Zero-shot wafer defect classification achieves high accuracy with prompt ordering
Zero-Shot Multi-Class Wafer Map Classification with Rule-Ordered Prompts for Large Multimodal Models
Artificial Intelligence
Summary
Classifying defects on semiconductor wafers usually needs lots of labeled examples and retraining when conditions change. This paper shows a way to classify six types of wafer defect maps without any new training, using a large pretrained AI model with special prompts that focus on features like shape and position. The authors tested their method on two large datasets and found it works very well, even without retraining. They also explored how changing the order of prompt rules affects accuracy.
What this means in practice
- •For semiconductor manufacturing engineers: Automatically classify wafer defect patterns without retraining even when production changes affect data distribution.
- •For quality control automation teams: Incorporate zero-shot defect classification into inspection workflows to reduce manual labeling and retraining effort.
Authors
Tianjian Cao, Jun Tao
Abstract
Wafer-map defect classification often requires labeled data and retraining when production conditions change. We present a training-free six-class framework that combines connected-component highlighting with morphology-, location-, and exclusion-aware prompts for a frozen large multimodal model. On Mixed-WM38 (6,866 maps), multi-region prompting achieves 92.9% macro and 93.8% overall accuracy. On WM811K (9,000 maps), it achieves 79.1% and 81.3%, respectively. Eight alternative rule orders achieve 90.1-91.9% overall accuracy, compared with 93.8% for the selected order.