Abstract
This paper addresses the issue of vacuum pump degradation in semiconductor manufacturing. The ability to identify the current level of vacuum pump degradation and predict the Remaining-Useful-Life (RUL) of a dry vacuum pump would allow manufacturers to schedule pump swaps at convenient times, and reduce the instances of unexpected pump failures, which can incur significant costs. In this paper, artificial neural networks are used to model the current level of pump degradation using pump process data as inputs, and a double-exponential smoothing prediction method is employed to estimate the RUL of the pump.We also demonstrate the benefit of incorporating process data, from the upstream processing chamber, in the development of a solution.
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