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

• URL: http://arxiv.org/abs/2002.10974v1
• Date: Tue, 25 Feb 2020 15:38:11 GMT
• Title: Fault Diagnosis in Microelectronics Attachment via Deep Learning Analysis of 3D Laser Scans
• Authors: Nikolaos Dimitriou, Lampros Leontaris, Thanasis Vafeiadis, Dimosthenis Ioannidis, Tracy Wotherspoon, Gregory Tinker, and Dimitrios Tzovaras
• Abstract summary: 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. 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. We propose a system that automates fault diagnosis by accurately estimating the volume of glue deposits before and even after die attachment.
• Score: 3.685017301279423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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