Sensor-based modeling of slurry chemistry effects on the material removal rate (MRR) in copper-CMP process |
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| Authors: | U Phatak S Bukkapatnam Z Kong R Komanduri |
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| Affiliation: | 1. Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078, USA;2. School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74078, USA;1. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA;2. Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA, USA;1. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA;2. Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA, USA;3. Department of Industrial and Systems Engineering, Virginia Tech University, Blacksburg, VA, USA;4. Emeritus Professor of Mechanical Engineering, University of Johannesburg, Johannesburg, South Africa |
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| Abstract: | Material removal rate (MRR) and surface quality in copper-chemical mechanical planarization (Cu-CMP) process are highly sensitive to slurry chemistry parameters, namely, pH, and concentrations of complexing, corrosion inhibiting, and oxidizing agents. Capturing the effects of these slurry parameters on MRR and surface quality in real time through the use of sensor signals is key to ensuring an efficient Cu-CMP process. In this paper, vibration sensor signals collected from the Cu-CMP experiments are used to capture the variations in various slurry parameters as well as their influence on the MRR. Two sensors, namely, a wired accelerometer (Kistler Model 8728A500, sampling at 5 kHz) and a single channel wireless accelerometer (Tmote Sky sensor node, sampling at 500 Hz) are mounted at two distinct locations on a LapMaster-12 bench-top polishing machine. Various statistical features related to time and frequency domain characteristics of the sensor signals are extracted. It was found that principal component regression models relating these features to MRR are significantly more accurate than the conventional statistical regression models that use process parameters (slurry chemistry settings) only to estimate MRR. |
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