LAUSR

Virtual metrology (VM) is often applied in semiconductor manufacturing when process measurements cannot be obtained without delay or without impacting the cycle time. When the process condition is varying, the modeling results can be inaccurate if a single VM model is adopted. Also, the large number of variables in the process data increases the difficulty of obtaining good predictions. To improve the accuracy of the metrology value prediction, it is necessary to select and apply only the significant variables in modeling. This paper proposes a just-in-time approach for VM modeling, along with a variable shrinkage and selection method that uses Gaussian process regression. The proposed method is validated using semiconductor process data and found to be superior compared to conventional methods.