Abstract
This paper presents an architecture of a goal directed, adaptive computer vision system. Principally, the system will be used for automatically detecting faulty Integrated Circuits (ICs). We work on the inspection problem in its entirety of image segmentation, 2D structure identification and 3D object interpretation. At first, 3D model objects which represent IC components are geometrically projected into the 2D space to get a 2D model of the image structures. Afterwards, procedures for extracting regions and identifying complex image structures are adaptively executed by taking the 2D model into account. Finally, the 3D model objects are validated and modified according to the actual identifications. A CCD camera which is tied to a microscope is used for taking the images. The focus plane of the microscope can be steered horizontally and vertically to change focus and position on the top of the IC. Based on the modifications of the 3D model, the section of interest will be changed automatically.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literature
D.H. Ballard: Reference Frames for Animate Vision; Proceedings of the IJCAI, pp. 1635–1641,1989.
C.K. Cowan, P.D. Kovesi: Automatic Sensor Placement from Vision Task Requirements; IEEE Transactions on PAMI 10, No. 3, pp. 407–416, 1988.
P.D.S. Irwin, A.J. Wilkinson: An Integrated Segmentation/Image Analysis System; Lecture Notes in Computer Science 301, (ed.)J. Kittler, Pattern Recognition, pp. 26–37, 1988.
R. Jain, A.R. Rao, A. Kayaalp, et al.: Machine Vision for Semiconductor Wafer Inspection; pp. 283–314; in H. Freeman (ed.): Machine Vision for Inspection and Measurement; Academic Press, Inc. San Diego, USA, 1989.
Ken-Ichi Kanatani: 3D Euclidean Versus 2D Non-Euclidean: Two Approaches to 3D Recovery from Images; IEEE Transactions on PAMI 11, No. 3, pp. 329–332, 1989.
C.A. Kohl, A.R. Hanson, E.M. Riseman: Goal-Directed Control of Low-Level Processes for Image Interpretation; Proceedings of the Image Understanding Workshop, pp. 538–551, 1987.
T. Matsuyama: Expert Systems for Image Processing: Knowledge-Based Composition of Image Analysis Processes; Computer Vision, Graphics and Image Processing 48, pp. 22–49, 1989.bonding
M.E. Mortenson: Geometric Modeling; John Wiley & Sons, New York, USA, 1985.
K.N. Ngan, B. Kang: Geometric Modelling of IC Die Bonds for Inspection; Pattern Recognition Letters 10, pp. 47–52, 1989.
J. Pauli: Knowledge-Based Adaptive Identification of 2D Image Structures; to appear in the Proceedings of the International Society for Photogrammetry and Remote Sensing; Commision V, 1990.
B. Radig: Image Sequence Analysis Using Relational Structures; Pattern Recognition 17, No. 1, pp. 161–167, 1984.
S. Ullman: Aligning Pictorial Descriptions: An Approach to Object Recognition; Cognition 32, pp. 193–254, 1989.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1990 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pauli, J. (1990). Goal Directed, Adaptive Computer Vision for IC Bond Inspection. In: Großkopf, R.E. (eds) Mustererkennung 1990. Informatik-Fachberichte, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-84305-1_67
Download citation
DOI: https://doi.org/10.1007/978-3-642-84305-1_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-53172-2
Online ISBN: 978-3-642-84305-1
eBook Packages: Springer Book Archive