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An automatic visual system to identify and estimate ionic contamination in printed circuit boards using electrochemical migration patterns

机译：一种自动视觉系统，可使用电化学迁移模式识别和估算印刷电路板中的离子污染

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Detection and estimation of ionic contamination in electronics is important, in order to ensure manufacturing quality by detecting the cause and preventing failure mechanisms such as electrochemical migration (ECM). However, such tests require expensive, specialized equipment. This paper proposes a low-cost, automatic, visual-based method, in which the ionic contamination profile of a printed circuit board is determined through the analysis of shape and color features of optical microscope images of ECM failures. Images of copper dendrites were acquired through the water drop test using solutions contaminated with NaCl and NaSO from 10-50 ppm, in steps of 10. Thresholding and connected component analysis were used to segment the shorting dendrite. The method used three types of features: global and local shape features, and color features. Two feature selection methods, ReliefF and Correlation-based Feature Selection (CFS) were also tested to measure feature quality and to determine the best feature subsets. The predictive model used was feature-weighted k-nearest neighbor. The study determined that copper dendrites produced with sodium sulfate contaminant were larger and denser compared to sodium chloride. Increasing the amount of contaminant also increased the density of the pattern. At higher sodium sulfate contamination levels, dendrites tended to have reddish tips, while with sodium chloride, branch shapes transitioned from a well-defined appearance to a “stringy” appearance. The system was able to distinguish between the two contaminants at 97.3%, while using only eight descriptors. The system was also able to distinguish between five closely-spaced contaminant levels at 63.38% and 57.14% accuracy for sodium chloride and sodium sulfate respectively. Local shape features, which were not used in previous work, were found to be generally more useful compared to global shape features.
机译：为了确保通过检测原因并防止诸如电化学迁移（ECM）之类的故障机制来确保制造质量，电子设备中离子污染的检测和评估非常重要。但是，这样的测试需要昂贵的专用设备。本文提出了一种低成本，自动的，基于视觉的方法，该方法通过分析ECM故障的光学显微镜图像的形状和颜色特征来确定印刷电路板的离子污染分布。通过水滴测试，使用10％至5​​0 ppm的受NaCl和NaSO污染的溶液，以10步进行水滴测试，获得铜枝晶的图像。使用阈值法和连接成分分析来分割短路枝晶。该方法使用了三种类型的特征：整体和局部形状特征以及颜色特征。还测试了两种特征选择方法，即ReliefF和基于相关性的特征选择（CFS），以测量特征质量并确定最佳特征子集。使用的预测模型是特征加权的k最近邻居。该研究确定，与氯化钠相比，由硫酸钠污染产生的铜树枝状晶体更大，更致密。污染物含量的增加也增加了图案的密度。在较高的硫酸钠污染水平下，树枝状晶体趋于带红色，而氯化钠的树枝状形状则从清晰的外观转变为“丝状”外观。该系统仅使用八个描述符就可以区分两种污染物，占97.3％。该系统还能够区分五个紧密间隔的污染物水平，分别为氯化钠和硫酸钠，准确度分别为63.38％和57.14％。发现以前的工作中未使用的局部形状特征通常比全局形状特征更有用。

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