Ionic contamination identification and estimation in printed circuit boards using visual characteristics of electrochemical migration patterns - Villanueva, Helen S. (Quezon City College of Engineering, University of the Philippines Diliman, 2014)
Ionic contamination is one of the significant causes of failure in electronics. Current methods to identify and quantify ionic contamination in printed circuit boards involve the use of expensive, specialized equipment. A failure mechanism induced by ionic contamination is electrochemical migration (ECM), which is becoming a significant problem due to the increasing miniaturization of electronic components. This study proposes a fast, low-cost, automatic visual-based system to identify the type of ionic contaminant and estimate the amount present for ECM failures. Using the failure pattern produced during electrochemical migration, image processing and machine learning techniques may be combined to automatically classify a sample based from shape and color features extracted from the dendrite images. To obtain the samples, the water drop test was performed to grow copper dendrites using solutions contaminated with NaCl and Na2SO4 from 10-50 ppm, in steps of 10. Images at the time-to-failure were taken from the ECM videos captured by a USB microscope. Global and local shape features were extracted from the shorting dendrite and color features in the HSV color space were extracted from the image as a whole. Features were selected using ReliefF ranking and Correlation-based Feature Selection. Classification was performed using feature-weighted k-nearest neighbor using ReliefF for feature weighting. The proposed method was able to identify the type of contaminant with accuracy up to 97.30% using a feature subset of size 7 using CFS. Accuracy for contaminant estimation was lower, at 63.38% using 8 features and 57.14% using 13 features with ReliefF for sodium chloride and sodium sulfate respectively. The loss of accuracy for contaminant estimation was explained by the high variability of the appearance of the dendritic failure pattern and the high similarity of structures at very close levels, due to natural growth factors, equipment limitations, and human errors in sample gathering. The use of a non-visual feature, time-to-failure, was shown to improve accuracy to uo to 67.61% and 66.23% for sodium chloride and sodium sulfate respectively while reducing the number of features.
Thesis (M.S. Computer Science)--University of the Philippines, Diliman.
Keywords: Printed circuits; Ionic contamination; Electrochemical migration (ECM).