Methodology for Plasma Diagnosis and Accurate Virtual Measurement Modeling Using Optical Emission Spectroscopy

In this study, we diagnose and monitor the plasma process in real time using optical emission spectroscopy (OES). Notably, this method is inexpensive and can effectively diagnose the plasma state without plasma interference. The virtual metrology (VM) model based on machine learning can successfully predict the thickness of a ZrO2 thin film deposited via plasma-enhanced atomic layer deposition (PE-ALD). The neural network model predicts the thickness by recording the emission light information generated during PE-ALD using OES. The prediction accuracy can be improved by including the maximum possible number of process variables, such as the radio frequency (RF) power, pressure, and gas sensing data, in modeling. However, the complexity of the system may increase owing to the requirement of physical knowledge on the system and result interpretation. Therefore, herein, we perform predictive modeling using variables with a high correlation and OES data, focusing on the importance of each process variable. Additionally, we use plasma data with minimized variability for variable optimization of the PE-ALD process. Consequently, we design an enhanced VM model with high prediction accuracy. The methodology adopted in this study is based on the PE-ALD process; however, it can also be extended to other processes using plasma.