Detection and identification of particles on silicon wafers based on light scattering and absorption spectroscopy and Machine learning

Abstract

Modern semiconductor manufacturing technology have a high-quality requirement of the wafers, and therefore the wafer inspection technique becomes increasingly important. During the manufacturing processes, particles can attach on the surface of the wafer which is an important factor of the quality and can even make it impossible to use the wafer. In this research, we introduce a particle detection and identification method based on the scattering and absorption spectra of the particles. A machine learning algorithm was developed to capture the feature of the particles and is able to identify the particle material from the scattering spectrum. Three different particles (Al2O3, SiC, and Si) were used to test this system. The validation accuracy achieves higher than 98% after 5 iterations training. The system was tested by scattering these three particles on the same wafer in different regions without mixing with each other. The results shows that particle Al2O3 and Si were identified with a high accuracy, whereas it is still challenging for the system to correctly label SiC particles. This can be improved by a larger dataset to enhance the generalization ability of the machine learning model.