Journal
ELECTRONICS
Volume 10, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/electronics10010049
Keywords
end point detection; plasma etching; CNN; SVM; adaboost ensemble
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Funding
- Institute of Information & communications Technology Planning & Evaluation (IITP) - Korea government (MSIT) [2020-0-02102-001]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2020-0-02102-001] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This paper proposes an enhanced optimal EPD method based on CNN for plasma etching process, which outperforms traditional methods in performance.
As the technology node shrinks and shifts towards complex architectures, accurate control of automated semiconductor manufacturing processes, particularly plasma etching, is crucial in yield, cost, and semiconductor performance. However, current endpoint detection (EPD) methods relying on the experience of skilled engineers result in process variations and even errors. This paper proposes an enhanced optimal EPD in the plasma etching process based on a convolutional neural network (CNN). The proposed approach performs feature extraction on the spectral data obtained by optical emission spectroscopy (OES) and successfully predicts optimal EPD time. For the purpose of comparison, the support vector machine (SVM) classifier and the Adaboost Ensemble classifier are also investigated; the CNN-based model demonstrates better performance than the two models.
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