Virtual metrology modeling of reactive ion etching based on statistics-based and dynamics-inspired spectral features

Due to increasing demand on the fabrication yield and throughput in micro/nanoscale manufacturing, virtual metrology (VM) has emerged as an effective data-based approach for real-time process monitoring. In this work, a novel automated methodology, without the need for domain knowledge and experience, for extracting useful features from raw optical emission spectroscopy (OES) data is presented. Newly proposed OES features are combined with other types of data, which include tool settings, sensor readings, physical measurements, non-numerical data, and process control parameters. Using partial least squares and support vector regression, VM models for predicting the critical dimension after reactive ion etching are built. The results from the VM model indicate that the coefficient of determination of up to 0.65 and the root mean square Error of 0.08 can be achieved. Compared to the traditional features obtained by the current solution in industry, the performances of VM models via the proposed methodology can enhance the coefficient of determination by 62.5% and reduce the root mean square error by 23.1%.