Abstract
We analyzed how the features in plasma information based virtual metrology (PI-VM) for SiO2 etching depth with
variation of 5% contribute to the prediction accuracy, which is previously developed by Jang. As a single feature, the
explanatory power to the process results is in the order of plasma information about electron energy distribution
function (PIEEDF), equipment, and optical emission spectroscopy (OES) features. In the procedure of stepwise variable
selection (SVS), OES features are selected after PIEEDF. Informative vector for developed PI-VM also shows
relatively high correlation between OES features and etching depth. This is because the reaction rate of each chemical
species that governs the etching depth can be sensitively monitored when OES features are used with PIEEDF.
Securing PIEEDF is important for the development of virtual metrology (VM) for prediction of process results. The
role of PIEEDF as an independent feature and the ability to monitor variation of plasma thermal state can make other
features in the procedure of SVS more sensitive to the process results. It is expected that fault detection and
classification (FDC) can be effectively developed by using the PI-VM.
Key Words : Silicon oxide etching, Plasma information (PI) variable, Virtual metrology (VM), Optical emission
spectroscopy (OES), Statistical selection method, PI-VM