Effect of Dispensing Type on Void Formation Using Convolutional Neural Network

Author:

Azahari Muhammad Taufik1,Ling Calvin1,Abas Aizat1,Ng Fei Chong1

Affiliation:

1. School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus , Nibong Tebal, Penang 14300, Malaysia

Abstract

Abstract Underfilling in flip chip packages is a critical component of reliability. This study utilized I-type, L-type, and U-type dispensing methods to address the issue, namely, voiding that creates empty spaces, which compromises reliability. An automated solution using convolutional neural network (CNN) is proposed for void detection in chip images to replace the conventional manual inspection approach. The CNN model built on MobileNetV2 attains a mean average precision of 0.533. This method calculates void percentage, adhering to Institute for Interconnecting and Packaging Electronic Circuits (IPC) standards, to determine product acceptance or rejection, offering an efficient solution for quality control in flip-chip package manufacturing.

Publisher

ASME International

Reference18 articles.

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