Growth of pixel density and sensor array size increases the likelihood of developing in-field pixel defects. An ongoing study on defect development in imagers has now provided us sufficient data to be able to quantify characteristics of defect growth. Preliminary investigations have shown that defects are distributed randomly and the closest distance between two defective pixels is approximately 79-340 pixels apart. Furthermore, from an observation of 98 cluster-free defects, the diameter of the defect is estimated to be less than 2.3% of a pixel size at 99% confidence level. The fact that no defect clusters were found in the study of various digital cameras allows us to conclude that defects are not likely to be related to material degradation or imperfect fabrication but are due to environmental stress such as radiation. Furthermore, as verified by a statistical study, the absence of defect clustering provides information on the size of defects and insight into the nature of the defect development.

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The reliability of solid-state image sensors is limited by the development of defects, particularly hot-pixels, which we have previously shown develop continuously over the sensor lifetime. Our statistical analysis based on the distribution and development date of defects concluded that defects are not caused by single traumatic incident or material failure, but rather by an external process such as radiation. This paper describes an automated process for extracting defect temporal growth data, thereby enabling a very wide sample of cameras to be examined and studied. The algorithm utilizes Bayesian statistics to determine the presence and absence of defects by searching through sets of color photographs. Monte Carlo simulations on a set of images taken at 0.06 to 0.5sec exposures demonstrated that our tracing algorithm is able to pinpoint the defect development date for all the identified hot pixels within ±2 images. Although a previous study has shown that in-field defects are isolated from each other, image processing functions applied by cameras such as the demosaicing algorithm were found to cause a single defective pixel to appear as a cluster in a color image, increasing the challenge of pinpointing the exact location of hot defects.

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