Deep Learning For Corner Fill Inspection

Using deep learning in automated optical inspection of electronic assemblies to achieve fast, accurate solutions for object detection.


When automated optical inspection (AOI) works, it is almost always preferable to human visual inspection. It can be faster, more accurate, more consistent, less expensive, and it never gets tired. But there are some challenging applications. Some tasks that are very simple for humans are quite difficult for machines. Object detection is an example. Given an image containing a cat, a dog and a duck, a human can instantly confirm the objects’ presence, even when they overlap, and tell you exactly what points in the image are included in each one. This seemingly simple task can be very challenging for AOI. In electronic assembly, manufacturers may want to confirm the presence or absence of a component that varies little in position or appearance from assembly to assembly. This is a relatively simple task. A more difficult problem, and the focus of the work described here, is the detection of corner fill used to secure integrated circuits (IC) to a substrate. Though the location of the fill is relatively constant, its shape and size may vary from instance to instance. This variability makes detection much more complicated.

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