Effect of Dispensing Type On Void Formation Using Convolutional Neural Network

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 propose 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 IPC standards, to determine product acceptance or rejection, offering an efficient solution for quality control in flip-chip package manufacturing.