Deep learning and analytical study of void regional formation in flip-chip underfilling process

Abstract

Purpose This paper aims to study the relationship between the ball grid array (BGA) flip-chip underfilling process parameter and its void formation region. Design/methodology/approach A set of top-down scanning acoustic microscope images of BGA underfill is collected and void labelled. The labelled images are trained with a convolutional neural network model, and the performance is evaluated. The model is tested with new images, and the void area with its region is analysed with its dispensing parameter. Findings All findings were well-validated with reference to the past experimental results regarding dispensing parameters and their quantitative regional formation. As the BGA is non-uniform, 85% of the test samples have void(s) formed in the emptier region. Furthermore, the highest rating factor, valve dispensing pressure with a Gini index of 0.219 and U-type dispensing pattern set of parameters generally form a lower void percentage within the underfilling, although its consistency is difficult to maintain. Practical implications This study enabled manufacturers to forecast the void regional formation from its filling parameters and array pattern. The filling pressure, dispensing pattern and BGA relations could provide qualitative insights to understand the void formation region in a flip-chip, enabling the prompt to formulate countermeasures to optimise voiding in a specific area in the underfill. Originality/value The void regional formation in a flip-chip underfilling process can be explained quantitatively with indicative parameters such as valve pressure, dispensing pattern and BGA arrangement.