The process monitoring and profile analysis are critical in detecting various abnormal events in semiconductor
manufacturing, which consists of highly complex, interrelated, and lengthy wafer fabrication processes for yield
enhancement and quality control. This study aims to develop a framework for semiconductor faults detection
and classification (FDC) to monitor and analyze wafer fabrication profile data from a large number of related
process variables to remove the cause of the faults and thus reduce abnormal yield loss. The purpose of this
study was to develop a process management system to manage ingot fabrication and improve ingot quality. The
ingot is the first manufactured material of wafers. The quality parameters were applied to evaluate the quality.
Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical
methods were used for data generation. The proposed framework can effectively detect abnormal wafers based
on a controlled Image Processing and KNN techniques are used. The extracted information can be used to
assist semiconductor faults diagnosis process recovery. The results demonstrate the practical applicability of
the wafer data Prediction.
Software And Hardware