Readiness for predictive maintenance of high cost EUV equipment using big data

The EUV (Extreme Ultra Violet) photomask manufacturer is focused on the challenges of high quality and works to photomask industry efforts to reduce defect in mass product operations and in other sectors of YMS (Yield Management System). The Tech Insight official said that the EUV photomask product market looks lucrative business because the mass product has been recording an annual CAGR (Compound Annual Growth Rate) growth rate of 1.8 percent in recent years. Moreover, more than 50% of all wafer scanners are models with ArF scanners or higher model. Therefore, Necessary to contribute to production with efficient management and no loss time for expensive EUV equipment. In a EUV era, every single second management is required and a big data analysis system is needed to detect fine changes. In this paper, we create big data using FDC (Fault Detection & Classification) function and use it to study cases of electron beam writer, process, etch, clean, repair, metrology, inspection, and pellicle equipment's maintenance and risk management. Photomask process excursion could be a result of one or more of degrading equipment part, or equipment issue of the photomask quality from any of the previous steps. Detecting such FDC data excursions and notifying appropriate fab or assembly/test personnel could result in preventing yield loss, improving cycle-time, OEE (Overall Efficiency Effectiveness) and equipment up-time. So far, we will share the research case that acquiring by accumulated knowledge and the facility maintenance method using FDC will accelerate the resolution of the most urgent problems.