Virtual Metrology in Semiconductor Manufacturing by Means of Predictive Machine Learning Models

Advanced Process Control (APC) is an important research area in Semiconductor Manufacturing (SM) to improve process stability crucial for product quality. In low-volume-high-mixture fabrication plants (fabs), Knowledge Discovery in Databases is extremely challenging due to complex technology mixtures and reduced availability of data for comparable process steps. High Density Plasma Chemical Vapor Deposition (HDP CVD) appears to be a process area in SM predestinated for application of Data Mining (DM). Enhancing physical metrology by predictive models leads to smart future fabs. Actual research focuses on Virtual Metrology (VM) using high sophisticated Machine Learning (ML) methods to model unknown functional interrelations and to predict the thickness of dielectric layers deposited onto a metallization layer of the manufactured wafers. Decision Trees (DT), Neural Networks (NN) and Support Vector Regression (SVR) have been investigated to maximize the accuracy of the regression. For data of various logistical granularities promising results have been achieved by implementing these statistical models.