A Comparative Study of Semiconductor Virtual Metrology Methods and Novel Algorithmic Framework for Dynamic Sampling

Virtual metrology (VM) is an important technology in semiconductor manufacturing that enhances process control, reduces costs, and improves quality. However, as processes become more complicated and process variations increase due to high-mix manufacturing, VM still faces challenges such as sensor drift and shift, as well as limited availability of metrology data due to high costs. This paper proposes an online Gaussian process (OGP) model designed to operate effectively with minimal initial metrology data and adapt dynamically to new data. The OGP model incorporates uncertainty quantification to optimize the sampling process, thereby enabling an adaptive sampling strategy to conduct metrology based on the process control needs. The proposed method is validated using a public dataset from the chemical mechanical planarization (CMP) process, demonstrating its effectiveness in tracking data drift and shift while reducing the required metrology data to retain model performance in an online operation setting.