Unpredictable Kirkendall void formation at the interface of circuit interconnections underlies degradation in electronics, yet there is a lack of effective approaches to curb the amount of these voids. Here we developed a strain-anneal method to tailor grain size distributions in the copper substrate of interconnections, and demonstrate quantitatively that not only the removal of the impurities but also an increase in the grain size of the substrates leads to an appreciable decline in the void density. The interconnections on the substrate recrystallized at a high annealing temperature show the massive porosity and the increased sensitivity of the voiding to the grain size. Our findings have broad implications for manipulation of void propensity in many other hetero-interfaces and are essential for high-performance circuit bonding in high temperature/high power electronic devices based on wide band gap semiconductors.
Purpose
This paper aims to provide the proper preset temperatures of the convection reflow oven when reflowing a printed circuit board (PCB) assembly with varied sizes of components simultaneously.
Design/methodology/approach
In this study, computational fluid dynamics modeling is used to simulate the reflow soldering process. The training data provided to the machine learning (ML) model is generated from a programmed system based on the physics model. Support vector regression and an artificial neural network are used to validate the accuracy of ML models.
Findings
Integrated physical and ML models synergistically can accurately predict reflow profiles of solder joints and alleviate the expense of repeated trials. Using this system, the reflow oven temperature settings to achieve the desired reflow profile can be obtained at substantially reduced computation cost.
Practical implications
The prediction of the reflow profile subjected to varied temperature settings of the reflow oven is beneficial to process engineers when reflowing bulky components. The study of reflowing a new PCB assembly can be started at the early stage of board design with no need for a physical profiling board prototype.
Originality/value
This study provides a smart solution to determine the optimal preset temperatures of the reflow oven, which is usually relied on experience. The hybrid physics–ML model providing accurate prediction with the significantly reduced expense is used in this application for the first time.
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