Semi-Supervised Deep Kernel Active Learning for Material Removal Rate Prediction in Chemical Mechanical Planarization.

Among them, the usage variables have an increasing trend and do not conform to the phase change characteristics.

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The results in Table 4 and Table 5 show that the prediction errors of the different models basically show a decreasing trend as the proportion of labeled samples increases.

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