Rnn Based Photo-Resist Shape Reconstruction From Scanning Electron Microscopy


In this paper we introduce several novel random neural network [Gelenbe89, Gelenbe90, Gelenbe93, Gelenbe99] based techniques to address a difficult `inverse problem' in semiconductor fabrication metrology. The problem is that of deducing a chip's vertical cross-section from two-dimensional top-down scanning electron microscope images of the chip surface. Our results are illustrated with a variety of real data sets. In semiconductor chip fabrication, photo resistive material is used as an overlay which will protect substrate areas (typically metal) which must remain on the chip after other unprotected substrate areas are etched off. The shape and size of the photo-resist material, at the submicron level, is therefore largely responsible for the shape and quality of the protected substrate. Critical dimension scanning electron microscopy (SEM) is used to determine this shape, and the research addressed in this paper proposes new methods using learning neural networks, combined with physical modelling, to accurately obtain surface shape information from SEM imaging.

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Proceedings of the International Joint Conference on Neural Networks



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Article; Proceedings Paper

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0033686668 (Scopus)

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