Citation

Uddin, Md Ryhan and Islam, Md Saiful and Farid, Fahmid Al and Uddin, Jia and Mansor, Sarina (2026) DiffWaferNet: a lightweight differential attention-based network for wafermap defect classification. PeerJ Computer Science, 12. e3947. ISSN 2376-5992

Text DiffWaferNet_ a lightweight differential attention-based network for wafermap defect classification.pdf - Published Version Restricted to Repository staff only Download (11MB)

Abstract

In the semiconductor manufacturing process, wafermap defect classification is crucial for ensuring product quality and reliability. Despite recent advances, underrepresented defect types in wafer datasets continue to suffer from poor performance due to severe class imbalance, and models with large parameter counts present high computational costs. To address these challenges, DiffWaferNet, a lightweight differential attention-based network, is proposed to improve the performance of minority defect classes while preserving the accuracy of majority classes. In this study, a Conditional Variational Autoencoder (CVAE) is employed to generate class-consistent synthetic data for underrepresented defect types, effectively balancing the dataset and enhancing model robustness. In the classification phase, DiffWaferNet utilizes a convolutional neural network (CNN) for robust feature extraction and applies a differential attention mechanism to enhance focus on critical regions of wafer maps. This mechanism empowers the model to dynamically learn attention weights that highlight the most relevant features for defect classification. Experimental results demonstrate that DiffWaferNet significantly improves classification performance, particularly for minor defect types, achieving consistent performance across all defect classes. Compared to baseline models, the proposed method ensures both robustness and high overall accuracy in wafer map defect classification. Furthermore, extensive benchmarking on the MixedWM-38K dataset validates the generalization capability of DiffWaferNet, achieving an accuracy of 97.88% and an F1-score of 97.76%. With only 121K parameters and 21.8M FLOPs (Floating-point Operations), the model maintains high computational efficiency, making it well-suited for real-time industrial applications. This research highlights a strong potential for advancing defect classification and establishes a foundation for future studies.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Oct 2026 01:37
Last Modified:	02 Oct 2026 01:37
URII:	http://shdl.mmu.edu.my/id/eprint/16820

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