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Noise-Robust Wafer Map Defect Classification via CNN-ESN Hybrid Architecture.

Hayeon Choi¹, Dasom Im¹, Sangeun Oh¹

¹Department of System Semiconductor Engineering, Sangmyung University, Cheonan 31066, Republic of Korea.

|March 28, 2026

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A new hybrid CNN-ESN model improves wafer map defect classification robustness against noise. This approach enhances semiconductor manufacturing yield monitoring by maintaining accuracy under perturbations without needing noise-aware training.

Area of Science:

Semiconductor Manufacturing
Artificial Intelligence
Machine Learning

Background:

Wafer map defect classification is crucial for semiconductor yield monitoring and root-cause analysis.
Current Convolutional Neural Network (CNN) models struggle with robustness against input perturbations and variability at test time.
Existing methods are often evaluated on clean datasets, limiting their real-world applicability.

Purpose of the Study:

To propose a hybrid CNN-echo state network (ESN) architecture for enhanced robustness in wafer map defect classification.
To improve model performance under various perturbation scenarios, particularly the clean train/noisy test (CT-NT) setting.
To investigate the mechanisms behind robustness improvements in the proposed hybrid model.

Main Methods:

Keywords:

convolutional neural network defect classification echo state network reservoir computing robustness analysis semiconductor manufacturing wafer map

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Developed a hybrid CNN-ESN model integrating spatial feature extraction (CNN) with sequential aggregation (ESN).
Utilized a multidirectional scanline strategy to convert 2D CNN feature maps into sequences for ESN processing.
Implemented a class-specific adaptive fusion mechanism to combine CNN and ESN features, evaluated using binary-consistent die-flip and Gaussian perturbations on the WM-811K dataset.

Main Results:

The hybrid CNN-ESN model achieved a 0.61 pp test accuracy improvement over a ResNet34-based CNN on clean data, with greater gains for rare and structurally patterned defects.
In the CT-NT scenario, the hybrid model maintained 87.30% accuracy, a significant 9.71 pp improvement over the CNN baseline (77.59% at σ = 0.10).
Robustness gains were class-dependent, particularly benefiting defects like Loc and Edge-Ring, and attributed to enhanced representation stability and bounded reservoir dynamics.

Conclusions:

The proposed CNN-ESN hybrid architecture significantly enhances robustness against input perturbations in wafer map defect classification.
The model demonstrates superior performance in noisy test conditions without requiring noise-aware training or prior knowledge of perturbations.
This approach offers a promising solution for reliable yield monitoring and root-cause analysis in semiconductor manufacturing under real-world conditions.