Machine learning–driven in situ process monitoring with vibration frequency spectra for chemical mechanical planarization

Abstract

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.