Inspection of complex electronic packages requires discrimination between the various materials used in such packages. Variations in the appearance of these materials and in the equipment''s illumination complicates the segmentation process. In addition, some materials have similar reflectance and absorption characteristics. As a result, the segmentation process is sensitive to small variations in the illumination settings, photoresponse nonuniformity, and contrast fluctuations. In this paper, we present two techniques that reduce these variations: (1) a new method to calibrate and correct the photoresponse characteristics of optical inspection systems, and (2) a method to automatically correct for contrast variations between the inspected packages. This results in a more repetitive appearance of the used packaging materials, which in turn results in improved segmentation performance. The photoresponse correction procedure, models the output of each photosite as a linear function of input illumination and the parameters of the model are measured. The response is corrected using image processing hardware. Experimental results show that the nonuniformity is corrected to within +/- 1 of the A/D dynamic range which agrees with the error analysis. The contrast adjustment method adjusts the image contrast based on histogram features and is adjusted using vendor and custom developed hardware. The relationship between the two techniques is also discussed.
The measurement of surface topography is an important inspection task as it provides useful information for process and quality control. A candidate technique for such an application is confocal imaging. The advantages of confocal imaging are that it is a noncontact measurement, can be operated at high speed (greater than 10 megapixels/sec) and submicron resolution, and provides height information in multilayered semitransparent materials. In this paper, we present a scheme for the fast processing of confocal images. The scheme consists of measuring the response function of the confocal system and deriving a deconvolution filter based on this response. The input signal is deconvolved in order to improve the depth resolution and then processed to identify significant peaks. These peaks represent the position of different surfaces in the object being inspected. For semitransparent materials, our scheme is capable of detecting up to two surfaces at a given location.