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<p>The implementation of Industry 4.0 technologies, especially big data infrastructures, enables easy access to large amounts of data from all stages of semiconductor manufacturing. In this study, we propose a novel methodology to reduce a large amount of sensor data collected from wire bonding machines, called machine signals, into a set of interpretable features relevant to the wire bond quality of chips. The methodology is applied to real-world operational machine signal data from the wire bonding process at NXP Semiconductors N.V. During the wire bonding process, several sensors actively monitor the process, generating signals as discrete multivariate time-series for each semiconductor device. The proposed methodology consists of the following steps. First, we extract features from the discrete multivariate time-series and train a baseline model with all features. Second, we use permutation feature importance to rank the relevance of signals and corresponding features with the goal of identifying the optimal signal and feature set. Finally, the classification performance when using the optimal signal and feature set is compared to the performance of the baseline model. We conclude that the dimensionality of the data can be significantly reduced without losing classification performance. The reduced dimensionality leads to highly interpretable classification results in a real-world wire bonding use case.</p>

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Interpretable chip-quality classification with signal and feature selection in wire bonding

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月4日

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  • Cas Rosman,
  • Kai Schelthoff,
  • Alp Akcay

摘要

The implementation of Industry 4.0 technologies, especially big data infrastructures, enables easy access to large amounts of data from all stages of semiconductor manufacturing. In this study, we propose a novel methodology to reduce a large amount of sensor data collected from wire bonding machines, called machine signals, into a set of interpretable features relevant to the wire bond quality of chips. The methodology is applied to real-world operational machine signal data from the wire bonding process at NXP Semiconductors N.V. During the wire bonding process, several sensors actively monitor the process, generating signals as discrete multivariate time-series for each semiconductor device. The proposed methodology consists of the following steps. First, we extract features from the discrete multivariate time-series and train a baseline model with all features. Second, we use permutation feature importance to rank the relevance of signals and corresponding features with the goal of identifying the optimal signal and feature set. Finally, the classification performance when using the optimal signal and feature set is compared to the performance of the baseline model. We conclude that the dimensionality of the data can be significantly reduced without losing classification performance. The reduced dimensionality leads to highly interpretable classification results in a real-world wire bonding use case.