Identification and classification of particle contaminants on photomasks based on individual-particle Raman scattering spectra and SEM images.

Particle contamination of photo masks is a significant issue facing the micro-nanofabrication process. It is necessary to analyze the particulate matter so that the contamination can be effectively controlled and eliminated. In this study, Raman spectroscopy was used in combination with scanning electron microscopy with energy analysis (SEM-EDX) techniques to study the contamination of individual particles on the photomask. From Raman spectroscopic analysis, the Raman bands of particles mainly contributed to the vibrational modes of the elements C, H, O, and N. Their morphology and elemental composition were determined by SEM-EDX. The sizes of the particles are mostly less than 0.8 μm according to the SEM image analysis. Hierarchical clustering analysis (HCA) of the Raman spectra of particles have shown that the particles can be classified into six clusters which are assigned to CaCO, hydrocarbon and hydrocarbon polymers, mixture of NHNO and few (NH)SO, mixtures metal oxides, D and G peaks of carbon, fluorescent and (NH)SO clusters. Finally, principal component analysis (PCA) was used to verify the correctness of the classification results. The identification and classification analysis of individual particles of photomask contamination illustrate the chemical components of the particles and provide insights into mask cleaning and how to effectively avoid particle contamination.