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- W2902317995 abstract "We propose a new feature extraction method for virtual metrology with multisensor data in semiconductor manufacturing based on deep autoencoder. The multistep process-equipment signals at wafer fabrication process consist of multiple subprocesses and signals thereof. Existing extracted features based on signals reconstructed by autoencoder turns into fluctuating shapes coarsely reflecting the original values. We propose the clipping fusion regularization on the signals reconstructed by deep autoencoder in order to extract features from raw signals preserving data characteristics. The predictive performance with the features extracted by the proposed model is evaluated in a case of an etching process for wafer fabrication. The experimental result shows that the proposed method improves the accuracy of prediction models, compared with those with the existing feature extraction approaches." @default.
- W2902317995 created "2018-12-11" @default.
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- W2902317995 date "2019-01-01" @default.
- W2902317995 modified "2023-09-25" @default.
- W2902317995 title "Deep Autoencoder With Clipping Fusion Regularization on Multistep Process Signals for Virtual Metrology" @default.
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- W2902317995 doi "https://doi.org/10.1109/lsens.2018.2884735" @default.
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