The application of a contrast measure for segmenting images and the relationship of this method to polynomial-based classification are considered. The relevance of the segmentation method is shown by applying the contrast measure to a semiconductor manufacturing inspection problem. The contrast measure is shown to be a particular case of a pixel classification technique based on polynomial decision functions and local texture features. This relationship integrates an important heuristic method with the rigorous theory of decision-theoretic classification.>

Machine vision methods are presented for the analysis of solder balls in integrated circuits. The algorithms are founded on counter fitting using a multiparameter Hough transform and on polynomial-classifier-based pattern recognition. The first method is used to show the complexity of the inspection problem, especially in the presence of high-precision requirements. In this connection, it is shown that subpixel accuracy is not obtainable even under the assumption of a perfect camera system which determines the resolution necessary for the measurement of a given maximum-volume distortion. The second method is carried out by computing a large number of features on the original image after individual solder balls are segmented by a projection technique. This approach can be considered as a control-free image segmentation paradigm, since it does not rely on properly sequencing several image-analysis modules. Further experimentation with a large pool of defective solder balls is necessary to confirm the applicability of these machine vision algorithms to a real-world manufacturing inspection systems. A general image-segmentation architecture is proposed, which enables the computation of the necessary low-level image features as well as pixel classification at video-rate speed.>

This paper describes the use of an image contrast measure for producing binary segmentations of images in a certain class of applications. This method is well-suited for fast pipeline implementations, because the contrast measure uses only two local features in the image. To eliminate segmentation noise, we post-process the segmentations using binary morphological operations. This method has been applied to three different microelectronics inspection problems, with consistently good results, and experimental results from each of these applications are presented here. Also, we discuss this technique in terms of the theory of polynomial classifiers.

We address the problem of segmenting and analyzing images of solder balls in integrated circuits. Two methods are explored, one based on projections computed along curved contours derived from the geometry of the problem, and the other using a supervised, feature-based polynomial classification method. High accuracy is required for the machine vision solution to be successful, and we have included some considerations concerning sub-pixel accuracy for the computation of the values of parameters determining the two-dimensional solder ball shapes. The classification technique has performed with satisfactory results on available data.

An important step in automatic visual inspection is verifying whether a part is good or bad, by comparing a list of inspection specifications to a list of extracted and measured defects. Our goal is to provide a general, flexible, and efficient solution to this problem. We present a solution following a rule-based approach for the case of specs for visual inspection of disk heads. However, due to the generality of our approach (within the realm of visual inspection), it is easily extendible to verification of specs in other visual inspection applications. While flexibility comes naturally with the rule-based approach, efficiency is still an issue. Therefore, we implemented two techniques to increase the efficiency of our system: one at the rule level, and one at the rule-matching level. We describe our implementation and show experimental results from applying our approach in an experimental system for automatic visual disk head inspection.

This paper deals with the problem of computing, projections of digital images. The novelty of our contribution is that we present algorithms which are suitable for implementation in general purpose image processing and image analysis pipeline architectures. Also, we propose some new pipeline configurations which achieve a remarkable degree of parallelism in the computation of projection data and in fact, of many other geometrical descriptors of digital images. In particular, random access memories and other dedicated hardware devices are not needed in our algorithms. The effectiveness of our approach and feasibility of the proposed architectures are demonstrated by running our algorithms in commercially available short-pipelines for image processing and analysis. Examples are shown of the use of projection data for machine vision applications.