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Nonstationary signal analysis and support vector machine based classification for vibration based characterization and monitoring of slit valves in semiconductor manufacturing

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Abstract

Slit valves play an important role in semiconductor manufacturing, enabling creation and maintaining of a vacuum environment required for wafer processing. Due to the high volume of production in the modern semiconductor industry, slit valves could experience severe degradation over their lifetime. If maintenance is not applied in due time, degraded valves may lead to defects in finished products due to pressure loss and particle generation. In this paper, we propose methods for signal processing and feature extraction for analysis of slit valve vibration signals. These methods are then used to demonstrate the ability to reliably, accurately and efficiently distinguish between vibration patterns of each individual valve via a multi-class classification procedure. Furthermore, instantaneous time–frequency entropy of valve vibrations enabled long term monitoring of a slit valve in production, in spite of variations in valve speed and operations.

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Notes

  1. Definitions of those properties, as well as mathematical constraints on the kernels that are necessary to achieve them are summarized in the seminal book by Cohen (1995).

  2. Observe all Mahalanobis distances between training vectors in class \(\nu _i\) and all Mahalanobis distances among training vectors in class \(\nu _j\). Then \(d(\nu _i,\nu _j,l)\) is the maximum of those distances. In a way, this is a measure of intra-class localization feature l provides for classes \(\nu _i\) and \(\nu _j\).

  3. Observe Mahalanobis distances from any training vector in class \(\nu _i\) to any training vector in class \(\nu _j\). Then \(d(\nu _i,\nu _j,l)\) is the minimum of those distances. In a way, this is a measure of inter-class separation feature l provides for classes \(\nu _i\) and \(\nu _j\).

  4. From each of the 4 segments, we got 16 time-domain based features and 19 time–frequency domain based features, for each of the three directions of vibrations, as well as the vibration RMS. In addition, the feature set also included the movement times of both closing and opening motions, yielding 1122 features.

  5. One should note that our classification method transformed this 50-class classification into 1225 pairwise classification problems, which were solved using the kNN classification algorithm.

  6. Those areas correspond to the portions of valve travel where increases in entropies were observed.

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Acknowledgements

This research is supported in part by the National Science Foundation (NSF) grant IIP 1266279. The content of this paper is solely the responsibility of the authors and does not represent the official views of the NSF.

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Authors and Affiliations

  1. Lam Research Corporation, Fremont, CA, USA

    M. Musselman

  2. Department of Mechanical Engineering, University of Texas, Austin, TX, USA

    H. Xie & D. Djurdjanovic

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  1. M. Musselman
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  2. H. Xie
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  3. D. Djurdjanovic
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Correspondence to D. Djurdjanovic.

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Musselman, M., Xie, H. & Djurdjanovic, D. Nonstationary signal analysis and support vector machine based classification for vibration based characterization and monitoring of slit valves in semiconductor manufacturing. J Intell Manuf 30, 1099–1110 (2019). https://doi.org/10.1007/s10845-017-1308-4

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