## 4 Conclusions and Future Work  
Wafer bin maps play an important role in yield improvement of semiconductor manufacturing. Automatic defect classification on WBMs can help engineers quickly locate problems on the production line and make timely adjustments. At present, most WBM automatic classification models require training with a large amount of labeled WBM data, and manual labeling is time-consuming. In practice, there are large-scale unlabeled WBMs that are not exploited. Therefore, determining how to use large-scale unlabeled data information to reduce the demand for labeled data is a key problem to be solved in semiconductor manufacturing.  
We proposed a two-step WBM automatic classification framework consisting of self-supervised pretraining and fine-tuning stages. Firstly, we introduced a self-supervised pretraining method based on momentum contrastive learning. Positive sample pairs were generated through data augmentation, while negative sample pairs were created using a queue of samples. By optimizing the contrastive loss, the model learned to extract features that characterize WBMs. Through ablation experiments, we demonstrated that momentum contrastive learning can effectively learn feature representations from a large amount of unlabeled wafer maps, improving the model's classification performance and reducing the need for labeled data.  
Additionally, to further enhance the training efficiency of labeled data, we proposed a fine-tuning method based on prototypical networks. The prototypical network is a mechanism for few-shot learning, where samples are classified based on their distances to the centers of each class. We compared our framework with two existing WBM defect classification works, and the results show that our method outperforms the others in terms of classification performance. Furthermore, we suggest removing labeled data with \"none\" defect type and only using eight specific defect patterns from the WM-811K dataset for fine-tuning. This approach improved the classification performance of WBMs with specific defects.  
Future work will focus on researching self-supervised learning and classification methods for WBMs with multiple defects, as in most practical cases, a wafer usually has multiple defects. A single-defect classification model cannot accurately classify wafer maps with multiple defects. Therefore, a practical issue is designing a multidefect classification model and combining it with self-supervised learning to enhance the model's representation ability. In future research, we will explore a framework for self-supervised learning and multidefect classification of WBMs.  
Figure 9: Visualization of the classification recall comparison with and without momentum contrastive learning.  
**Author Contributions:** Conceptualization, Z.H. and D.N.; methodology, Y.W., D.N. and Z.H.; software, Y.W.; validation, Y.W., D.N. and Z.H.; formal analysis, Y.W., D.N. and Z.H.; investigation, Y.W.; resources, Y.W.; data curation, Y.W.; writing--original draft preparation, Y.W. and D.N.; writing--review and editing, Z.H. and D.N.; visualization, Y.W., D.N. and Z.H.; supervision, D.N. and Z.H.; project administration, D.N.; funding acquisition, D.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Science Foundation China grant No. 62173298.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the Intel Corporation for supporting the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.