Fault Diagnosis in Microelectronics Attachment Via Deep Learning Analysis of 3-D Laser Scans

A common source of defects in manufacturing miniature printed circuits boards (PCBs) is the attachment of silicon die or other wire bondable components on a liquid-crystal polymer substrate. Typically, a conductive glue is dispensed prior to attachment with defects caused either by insufficient or excessive glue. The current practice in electronics industry is to examine the deposited glue by a human operator a process that is both time consuming and inefficient especially in preproduction runs where the error rate is high. In this article, we propose a system that automates fault diagnosis by accurately estimating the volume of glue deposits before and even after die attachment. To this end, a modular scanning system is deployed that produces high-resolution point clouds whereas the actual estimation of glue volume is performed by (R)egression-Net (RNet), a three-dimensional (3-D) convolutional neural network (3DCNN). RNet outperforms other deep architectures and is able to estimate the volume either directly from the point cloud of a glue deposit or more interestingly after die attachment when only a small part of glue is visible around each die. The entire methodology is evaluated under operational conditions where the proposed system achieves accurate results without delaying the manufacturing process.