  A common source of defects in manufacturing miniature Printed Circuits Boards
(PCB) is the attachment of silicon die or other wire bondable components on a
Liquid Crystal Polymer (LCP) 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 paper 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 3D 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.
