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Deep Learning Based Morphological Solder Segmentation

In this work, we will address the challenging problem of segmenting solder-joints from different base-materials using machine learning methods. This problem has up to now not received much attention. The optical images for this task were obtained by an Optical Microscope (OM). These optical images can help to evaluate the quality of solder joints by identifying the soldered area and the diffusion area of a solder joint between solder and base material. Together, they define the solder-spreading ratio, which helps to understand material properties, electrical behavior prediction and development of new solder-base material pairs. This method also might help in the production of solder joints for in-situ quality inspection. The used model deals as a baseline to the problem of morphological solder segmentation. It performs better than other state of the art methods. To the best of our knowledge, this is one of the first works in morphological solder segmentation using deep convolutional neural networks and it can be extended to other domains of surface inspections.