An Optical Inspection System for the Solder Balls of BGA using Support Vector Machine Classification
In this paper, the Support Vector Machine (SVM) is utilized to classify the defective solder ball of ball grid array (BGA) so as to promote the accuracy and efficiency of the optical inspection. The terms of inspections include the ball displacement, the ball over scale, the ball undersize, the ball absence, and ball bridging. There are two step of the SVM processing: the training step and the real testing step. The SVM is more efficiency because of the calculation in testing step is no relation to the number of the input vector. As this point, SVM is fit to build up the high dimension input recognize system such as BGA inspection. From the real testing results, the Support Vector Machine is proved to properly identify and classify the shape defects. The two dimensional BGA optical inspecting system is implemented by Visual Basic as the developing tool incorporated with the Halcon's function which is the database of the image processing on Windows operation system. For the development of the processing procedure of the automatic optical inspecting system with the designed lighting system, the precise geometrical information of the solder ball is evaluated by the sub-pixel method to identify the shape of solder ball and its location under the BGA board is offset and rotated at any angle.
E-quality control: A support vector machines approach
Abstract The automated part quality inspection poses many challenges to the engineers, especially when the part features to be inspected become complicated. A large quantity of part inspection at a faster rate should be relied upon computerized, automated inspection methods, which requires advanced quality control approaches. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of support vector machine (SVM) learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments and analyzed with different kernel functions. From the analysis, detailed outcome is presented for six different cases. The results indicate the robustness of support vector classification for the experimental data with two output classes.
Photoinduced spin-state transition of Co3+ in the layered perovskite manganite thin film
The photoinduced effect, at different temperatures, on layered perovskite manganite La1.2Sr1.8Mn1.8Co0.2O7 thin film deposited on a SrTiO3(100) substrate has been studied using a He–Ne laser (632.8 nm) with the power of output 25 mW at low temperatures. The electrical transport and magnetic properties of the film are investigated systematically in the temperature region from 20 to 300 K. The resistance shows a significant decrease at low temperatures (T<90 K) under laser irradiation, which can be interpreted in terms of the spin-state transition of Co3+ ions. The magnetic field combined with the laser is also used to detect the photoinduced effect of the spin-glass state. The time dependence of the metastable system under several applied fields was also discussed. The system has the ability to keep a persistent memory of the magnetic history imprinted in its zero-field resistance.