MAM (mapping and alignment model) for inspection data in semiconductor industry
Résumé
Increasing model to hardware gaps has turned technology development process into a high cost R&D activity, hence a new technology is developed in an alliance which is transferred, aligned and adapted for every product. Our engineers are focused on reducing the technology adaption and alignment lead times based on an efficient and effective root cause analysis but they spend significant amount of time in data extraction, mapping and alignment because available inspection data vary in format and coordinate system depending on tool and vendor. Today we have a huge volume of data in multiple dimensions but database issues (Shahzad et. al, 2011) limit our capabilities leading to an opportunity loss. In this article we present MAM (mapping and alignment model) for inspection data to ensure site to site mapping between PT and Inline data, die to die mapping and alignment between EWS and defectivity data and die to site qualification between PT/Inline and EWS/Defectivity data. It empowers our engineers to quickly find the root causes, classify them as systematic or random and transform them into rules and models for the faster ramp-up-rate.