Title

Rnn Based Photo-Resist Shape Reconstruction From Scanning Electron Microscopy

Abstract

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.

Publication Date

1-1-2000

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Volume

5

Number of Pages

221-226

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0033686668 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0033686668

This document is currently not available here.

Share

COinS