Abstract: Capacitive Lead Frame Testing, a widely used approach for printed circuit board testing, is very effective for open solder detection,. The approach, however, is affected by mechanical variations during testing and by tolerances of electrical parameters of components, making it difficult to use threshold based techniques for defect detection. A novel approach is presented in this thesis for identifying boardruns that are likely to be outliers. Based on Principal Components Analysis (PCA), this approach treats the set of capacitance measurements of individual connectors or sockets in a holistic manner to overcome the measurement and component parameter variations inherent in test data.
Effectiveness of the method is evaluated using measurements on different types of boards. Based on multiple analyses of different measurement datasets, the most suitable statistics for outlier detection and relative parameter values are also identified.
Enhancements to the PCA-based technique using the concept of test-pin windows are presented to increase the resolution of the analysis. When applied to one test window at a time, PCA is able to detect the physical position of potential defects. Combining the basic and enhanced techniques, the effectiveness of outlier detection is improved.
The PCA based approach is extended to detect and compensate for systematic variation of measurement data caused by tilt or shift of the sense plate. This scheme promises to enhance the accuracy of outlier detection when measurements are from different fixtures. Compensation approaches are introduced to correct the abnormal measurements due to sense-plate variations to a normal and consistent baseline. The effectiveness of this approach in the presence of the two common forms of mechanical variations is illustrated. Potential to use PCA based analysis to estimate the relative amount of tilt and shift in sense plate is demonstrated.
Adviser: Anura P. Jayasumana Co-Adviser: Yashwant K. Malaiya Non-ECE Member: N/A Member 3: Steven C. Reising Addional Members: N/A