Wafer map defect recognition based on multi-scale feature fusion and attention spatial pyramid pooling

Shouhong Chen, Zhentao Huang, Tao Wang, Xingna Hou () and Jun Ma
Additional contact information
Shouhong Chen: Guilin University of Electronic Technology
Zhentao Huang: Guilin University of Electronic Technology
Tao Wang: Guilin University of Electronic Technology
Xingna Hou: Guilin University of Electronic Technology
Jun Ma: Guilin University of Electronic Technology

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 14, 284 pages

Abstract: Abstract Wafers are products in semiconductor manufacturing and serve as the foundation for producing semiconductor chips. During the wafer testing stage, functional and electrical parameters are examined to identify defects in chip design and fabrication. The wafer map is the result of the wafer testing process. Analyzing and classifying defective information on the wafer map aids in defect source identification and optimization of the wafer production process. Deep learning has been employed for defect detection on wafer maps because of its superior image processing capabilities. Nevertheless, as semiconductor chip design integration and wafer size increase, more complex types of wafer defects tend to emerge in the production process, and the size, shape, and distribution of wafer defects can affect the final classification outcome. Accordingly, this study proposes a deep learning model, called ESPP-Net (Attention Spatial Pyramid Pooling Network), that combines a deep convolutional neural network and attention space pyramid pooling to recognize and classify single and mixed-type defect wafer maps. We evaluated our model on both the mixed-type dataset Mixed38WM and the single-type dataset WM-811K and compared it with state-of-the-art deep learning models. Experimental results show that our proposed model outperformed the preexisting models and demonstrated superior classification performance.

Keywords: Wafer map classification; Pattern recognition; Attention spatial pyramid pooling; Semiconductor manufacturing (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02231-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02231-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02231-z

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().