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Data-Driven Endpoint Detection for Small Open Area Etching Using Interpretable Transfer Learning
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AI
DOI：10.1109/TSM.2025.3643948.png)
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Abstract
En 中文
Accurate endpoint detection is critical in semiconductor plasma etching. Optical emission spectroscopy data contains information about the endpoint, but its analysis requires significant domain knowledge, and it contains large noise when the open area ratio is small. This paper proposes an interpretable data-driven endpoint detection method for small open area etching, leveraging transfer learning. First, we propose a formal metric quantifying the significance of endpoint trend in each wavelength signal and select the wavelength signals that are most sensitive to endpoint variations. Second, we devise an asymmetric autoencoder to uncover the endpoint trend in the noisy signals. Its asymmetric architecture incorporates nonlinear characteristics while ensuring the latent feature reflects the endpoint trends of the individual signals. Experimental results show that the model that learned the large open area etching (11.1%) can detect the endpoints of the small open area etching (0.5%) with the relative mean absolute error less than 0.6%, and amplify the signal-to-noise ratio by a factor of 2-3. Furthermore, the analysis of the selected wavelengths provides deeper insights into the underlying physical processes. The proposed method can be applied with minimal domain knowledge, while its results allow for exploring physical interpretations.
Keywords:
Etching
Market research
Feature extraction
Autoencoders
Plasmas
Transfer learning
Liquid crystal displays
Scanning electron microscopy
Principal component analysis
Noise
Optical emission spectroscopy
plasma etching
endpoint detection
data-driven
transfer learning
autoencoder
Journal
I
IF:
2.3
Papers:
64
Citations:
0
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