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MM-WAE: Multimodal Wasserstein Autoencoders for Semi-Supervised Wafer Map Defect Recognition.

Yifeng Zhang1,2, Qingqing Sun1, Ziyu Liu1

  • 1School of Microelectronics, Fudan University, Shanghai 200433, China.

Micromachines
|March 28, 2026
PubMed
Summary

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This study introduces a multimodal Wasserstein autoencoder (MM-WAE) for wafer map defect classification. The novel method enhances accuracy and robustness, especially for rare defect types, by effectively using limited labeled data.

Area of Science:

  • Semiconductor Manufacturing
  • Artificial Intelligence
  • Computer Vision

Background:

  • Wafer map defect pattern recognition is crucial for integrated circuit manufacturing yield.
  • Existing deep learning methods struggle with scarce data, imbalanced classes, and limited feature representation, impacting minority defect class performance.

Purpose of the Study:

  • To develop a robust semi-supervised classification method for wafer maps.
  • To address challenges of data scarcity, class imbalance, and complex defect morphologies in defect pattern recognition.

Main Methods:

  • Proposed a multimodal Wasserstein autoencoder (MM-WAE) with parallel spatial, frequency, and texture feature branches.
  • Employed multi-head attention and gating for adaptive multimodal fusion, characterizing defects comprehensively.
Keywords:
class imbalancemultimodal Wasserstein autoencodersemi-supervised learningwafer map defect recognition

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  • Utilized Wasserstein autoencoder with MMD loss for latent space regularization and incorporated inverse class-frequency weighted cross-entropy and modality consistency losses.

    • Main Results:

    • The MM-WAE model significantly improved accuracy and robustness in wafer defect classification.
    • Demonstrated effective mitigation of performance degradation caused by insufficient labels and class imbalance.
    • Showcased comprehensive characterization of defect patterns through fused multi-domain features.

    Conclusions:

    • The MM-WAE offers a promising solution for industrial wafer defect classification, particularly in data-scarce and imbalanced scenarios.
    • The method's ability to leverage unlabeled data enhances its practical applicability.
    • Further development holds potential for advancing semiconductor manufacturing yield optimization.