DETECTING SEMICONDUCTOR DEFECT FEATURES AND CLASSIFYING WAFER
DOI: https://doi.org/10.59982/18294359-23.14-ds-16
Abstract
This scientific work focuses on the development of a software tool for semiconductor defect feature detection and wafer classification. The tool utilizes advanced image processing techniques to extract relevant information from semiconductor wafers and classify them based on their defects. The development process involves the integration of various algorithms and machine learning models to optimize the tool’s performance. The results of the study demonstrate the effectiveness of the tool in accurately detecting and classifying semiconductor defects, which can aid in improving semiconductor manufacturing processes and reducing defects. The proposed software tool has the potential to become an essential tool for the semiconductor industry and contribute to the advancement of semiconductor technology.
The aim of the work is to classify semiconductor disk (wafer) defects. The input data, presented in tabular form, contains the coordinates of physical defects in the disk coordinate system, their sizes, belonging to a particular production process. Based on the location of defects and their properties, the processed software classifies disks into groups in order to clarify and eliminate further causes of damage.
Keywords: wafer, C++, microelectronic, wafer defect.
PAGES : 141-146