Title

Photoresist Shape Reconstruction From Secondary Scanning Electron Microscopy

Abstract

The paper discusses the approach of using single detector system to classify the photo resist surface using different signal and image processing methodologies. We couple the understanding of the physical phenomenon and come up with an integrated method to generally classify the surface depending upon the side wall curvature. Construction of 3-D image of the surface and extracting features from the methods to classify the surfaces are the predominant aspects of the approach. We have used several methods to serve the purpose including the wavelet filters in edge and surface roughness detection. In this paper, non-stereoscopic 3-D shape reconstruction method is applied 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 an image processing approach combined with physical modeling, to accurately obtain surface shape information from SEM imaging.

Publication Date

12-1-2001

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

4562 II

Number of Pages

813-821

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.458364

Socpus ID

0035766361 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0035766361

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