VDGPG: A virtual data-guided prompt generation framework for incremental learning with application to wafer defect detection

Publication Type

Conference Proceeding Article

Publication Date

10-2025

Abstract

Although convolutional neural networks have been widely used for wafer defect detection in semiconductor manufacturing, they typically rely on static offline datasets to train models. These models show strong reliability when detecting known defect types, but struggle with unknown ones, posing challenges in model adaptation and leading to high maintenance costs. Incremental Learning (IL) offers a solution that allows models to continuously adapt to new types of defects without accessing full historical data. This paper introduces a Virtual Data-Guided Prompt Generation (VDGPG) framework, a novel IL approach for wafer defect detection that integrates prompt-guided learning and dual-branch virtual data generation. Specifically, VDGPG assigns task-specific prompt vectors to individual attention heads, using a channel attention gating mechanism and similarity computation to select prompt vectors from a prompt pool. This enables the model to focus more effectively on the relevant features for each category of defects. The dual-branch virtual data generation module generates diverse virtual samples, with a special emphasis on contour edge generation, which guides the model to learn features of potential new categories proactively. Experiments using the public WM-811K dataset demonstrate that VDGPG achieves significant performance improvements in wafer defect detection over existing IL methods.

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, October 5-8

First Page

7074

Last Page

7079

Identifier

10.1109/SMC58881.2025.11343177

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/SMC58881.2025.11343177

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