A memory manufacturer uses fill under the corners of ICs to bond the package to the substrate. They needed to inspect for the presence or absence of fill and ensure that there is neither too much, nor too little. They required a solution that could measure the length of the corner fill and compare it to specifications. Traditional methods of corner fill inspection, such as blob analysis, are challenged by the lack of gray level specificity in this application.

Want to know, how we get this Conclusion? We have described the use of an AOI system (Nordson TEST & INSPECTION MRS-Enabled SQ3000™) and an associated deep learning algorithm to inspect corner fill in an electronics assembly application. The results confirm robust performance in detecting the presence or absence of corner fill and measuring its length. The system was easy to train and runs readily on a standard PC. Integration of the deep learning algorithm into standard system software would allow factory engineers to train networks that could then be run locally for inspection. There are many more potentially valuable applications for deep learning object detection in SMT and semiconductor applications that we are actively pursuing.