Improvements to X-ray laminography for automated inspection of solder joints

With the increased usage of fine-pitch assemblies and ball grid array (BGA) packages, there is a dramatic increase in demand for automated defect detection techniques such as X-ray laminography. However, the limitations of this imaging medium are not well understood by the industry. This article addresses the need for improving the imaging resolution of X-ray laminography, particularly for accurate three-dimensional (3-D) measurement of solder joint structures. The authors have developed a new method for reconstruction of the laminographs which improves the signal-to-noise ratio (SNR) of the laminographs significantly and enables better 3-D visualization of solder shape. Application of automated solder joint defect classification using neural networks has also been studied. Components with BGA, gull-wing and J-lead joints were imaged and several neural network methods were used to identify different classes of defects particularly significant to each type of joint. A novel probabilistic neural network approach for two-dimensional (2-D) image classification has been developed which performs as well as or better than a conventional backpropagation network.