The objective of this work is to tackle the challenges of monitoring and detecting subtle process changes in chemical mechanical planarization (CMP), an ultraprecision manufacturing process. Monitoring ultraprecision processes is usually of difficulty due to their innate complexity and low signal-to-noise ratio in sensor signals. Especially for subtle signal variations during small process changes, the conventional statistical process control charts could fail to detect such process anomalies from the sensor signals in the time domain. In this paper, frequency spectra representation of the microelectromechanical systems (MEMS) vibration sensor signals during subtle process changes is investigated, and the signal patterns uncovered by frequency spectra are utilized to formulate a machine learning–driven in situ process monitoring approach to detect process anomalies in CMP. The proposed approach overcomes the obstacles of differentiating subtle signal changes by transforming them into the frequency domain with Fourier transform and Hilbert-Huang transform and classifying the resulted frequency spectra with random forest. Based on frequency analysis, it can unveil the differences in the signals obscured in the time domain and suppress the high-frequency noise. Consequently, the presented machine learning–driven in situ process monitoring approach detects process anomalies by differentiating the deviated frequency spectra with machine learning. It is validated on our experimental CMP testbed for anomaly detection, and outperforms three benchmark statistical process control charts. For instance, it detects a slurry shutoff anomaly in CMP about ten times faster than the benchmark methods.

这项工作的目的是解决在超精密制造工艺化学机械平面化（CMP）中监视和检测细微工艺变化的挑战。由于其固有的复杂性和传感器信号的低信噪比，监视超精密过程通常很困难。尤其是对于在较小的过程变化过程中细微的信号变化，常规的统计过程控制图可能无法在时域中从传感器信号中检测到此类过程异常。在本文中，研究了微机电系统（MEMS）振动传感器信号在细微变化过程中的频谱表示，频谱发现的信号模式被用于制定机器学习驱动的原位过程监控方法，以检测CMP中的过程异常。所提出的方法通过用傅里叶变换和希尔伯特-黄变换将它们转换到频域并用随机森林对频谱进行分类来克服区分细微信号变化的障碍。基于频率分析，可以揭示时域中模糊信号的差异，并抑制高频噪声。因此，本文提出的机器学习驱动的原位过程监控方法通过将偏离的频谱与机器学习区分开来检测过程异常。它已在我们的实验性CMP测试平台上进行了验证，可用于异常检测，并优于三个基准统计过程控制图。例如，它在CMP中检测到浆料关闭异常的速度比基准方法快十倍。