A Survey of Health Indicators and Data-Driven Prognosis in Semiconductor Manufacturing Process

Semiconductor device fabrication is considered today as one of the most complicated manufacturing process, characterized by an important complexity of production context, in an uncertain environment. To improve process efficiency and productivity, it is of prime importance for engineers to dispose of reliable indicators to drive decision-making on maintenance operations. To this end, terabytes of different data are collected during the manufacturing process, to feed statistical analysis tools and production management systems. Prognostics and health management (PHM) is defined as the discipline that links studies of failure mechanisms to system lifecycle management. Among the different approaches existing for prognosis, data-driven techniques learn models directly from monitored operational data related to system health. There is therefore a great interest in applying data-driven PHM methodologies to address semiconductor manufacturing issues. This paper surveys works on data-driven approaches for two issues of PHM methodologies with applications focused on semi-conductor manufacturing process: the development of indicators for health assessment, and prognostic methods.