Dynamic Clustering for Wafer Map Patterns using Self-Supervised Learning on Convolutional Autoencoders

Donghwa Kim, Pilsung Kang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Defect pattern analysis in wafer bin maps (WBM) plays a significant role in the semiconductor manufacturing process because it helps identify problematic steps or equipment so that process engineers can take appropriate actions to improve the overall yield. Clustering algorithms have been widely used to detect different defect patterns. However, most clustering algorithms, such as K-means clustering and self-organizing map, are required to determine the number of clusters in advance. To resolve this issue, we propose a self-supervised learning-based dynamic WBM clustering method. The proposed model first uses pseudo-labeled data, of which, the labels are dynamically determined by the Dirichlet process mixture model (DPMM). Thereafter, it is fine-tuned using pseudo-labels in a self-supervised manner. Experimental results based on the WM-811K dataset indicate that the proposed model not only outperforms the benchmark models but also demonstrates robustness to hyperparameters. In addition, the defect patterns identified by our model are more accurately and distinctively localized than those identified by the benchmark models.

Original languageEnglish
JournalIEEE Transactions on Semiconductor Manufacturing
Publication statusAccepted/In press - 2021


  • Clustering algorithms
  • Clustering methods
  • Convolutional autoencoder
  • Data models
  • Dirichlet process
  • Feature extraction
  • Image reconstruction
  • Semiconductor device modeling
  • Visualization
  • deep clustering
  • pseudo-labels
  • self-supervised learning
  • wafer maps.

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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