Improving Machine Calibration Performance through Systematic Feature Design in Semiconductor Manufacturing

In semiconductor manufacturing, discrepancies between the golden and target machines can lead to inconsistencies in product quality and reduced yield, highlighting the need for more effective calibration methods. Traditionally, machine calibration relies on using basic statistical features during the feature extraction step, which limits the ability to fully capture the complex characteristics of machine dynamics. This study aims to expand the spectrum of feature types that contribute to enhancing machine calibration performance. Experiments using a Hitachi Plasma Etching System demonstrate that the new method significantly reduces overall calibration errors, representing more than a twofold improvement over the traditional approach. This enhanced calibration accuracy ensures greater consistency between machines and manufacturing processes, thereby enhancing operational efficiency and minimizing downtime.