Quantitative measures for surface texture description in semiconductor wafer inspection

Abstract

The automation of visual inspection in semiconductor wafer processing is a very challenging task. In this paper we address the automatic description and measurement of surface textures in semiconductor wafers. Texture plays a critical role in inspecting surfaces that are produced at various stages in the inspection of semiconductor devices. In this paper we describe a novel scheme to characterize surface textures that arise in semiconductor wafer processing. The emphasis in our scheme is on quantitative measures that allow for accurate characterization of surface texture. The fractal dimension is a quantitative measure of surface roughness, and we have developed an algorithm to automatically measure this. We also present an algorithm to compute the orientation field of a given texture. This algorithm can be used to characterize defects such as 'orange peel'. Furthermore, we have used the qualitative theory of differential equations to devise a symbol set for oriented textures in terms of singularities. An algorithm has been devised to process an image of a defect and extract qualitative descriptions based on this theory. We present the results of applying our algorithms to representative defects that arise in semiconductor wafer processing.