Experimental nonlinear dynamics characterization and monitoring of chemical mechanical planarization (CMP) Process
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abstract
Complex interactions and often dynamic variations in the process parameters and state variables are known to influence material removal rate in the chemical mechanical planarization (CMP) process. Experimental investigations presented in this paper support the nonlinear stochastic characteristics of CMP dynamics. Prior modeling and monitoring efforts have attributed much of the complex patterns of the signals from the CMP process to extraneous noise. Consequently, these models have limited predictability. Sensor features that quantify the process nonlinearity are found to be effective surrogates to track variations in certain process parameters. A monitoring approach based on combining these nonlinear dynamic features with conventional statistical descriptors of sensor signals as well as process parameter settings were found to improve the tracking of material removal rate (MRR) - which is one of the most important performance variables in CMP-by over 20% (linear R2 was > 80%) compared to the use of conventional features in a linear regression setting. The results consistently hold for a variety of process conditions tested using a battery of designed experiments.
