Monitoring roughness and edge shape on semiconductors through multiresolution and multivariate image analysis

Photolithography is one of the most important processes in the production of integrated circuits. Usually, attentive inspections are required after this process, but are limited to the measurement of some physical parameters such as the critical dimension and the line edge roughness. In this paper, a novel multiresolution multivariate technique is presented to identify the abnormalities on the surface of a photolithographed device and the location of defects in a sensitive fashion by comparing it to a reference optimum, and generating fast, meaningful and reliable information. After analyzing the semiconductor surface image in different levels of resolutions via wavelet decomposition, the application of multivariate statistical monitoring tools allows the in-depth examination of the imprinted features of the product. A two level nested PCA model is used for surface roughness monitoring, while a new strategy based on ‘‘spatial moving window’’ PCA is proposed to analyze the shape of the patterned surface. The effectiveness of the proposed approach is tested in the case of semiconductor surface SEM images after the photolithography process. The approach is general and can be applied also to inspect a product through different types of images, different phases of the same production systems, or different processes.