Optimization of Passive Chip Components Placement with Self-Alignment Effect for Advanced Surface Mounting Technology

TL;DR

This paper proposes machine learning-based prediction and optimization methods to improve the placement accuracy of electronic components in surface mount technology, leveraging self-alignment effects during reflow soldering.

Contribution

It introduces a novel combination of support vector regression, random forest regression, and nonlinear optimization to enhance component placement precision in SMT.

Findings

RFR outperforms SVR in prediction accuracy

Optimization reduces placement error to an average of 25.57 μm

Method demonstrates potential for improved SMT component alignment

Abstract

Surface mount technology (SMT) is an enhanced method in electronic packaging in which electronic components are placed directly on soldered printing circuit board (PCB) and are permanently attached on PCB with the aim of reflow soldering process. During reflow process, once deposited solder pastes start melting, electronic components move in a direction that achieve their highest symmetry. This motion is known as self-alignment since can correct potential mounting misalignment. In this study, two noticeable machine learning algorithms, including support vector regression (SVR) and random forest regression (RFR) are proposed as a prediction technique to (1) diagnose the relation among component self-alignment, deposited solder paste status and placement machining parameters, (2) predict the final component position on PCB in x, y, and rotational directions before entering in the reflow process. Based on the prediction result, a non-linear optimization model (NLP) is developed to optimize placement parameters at initial stage. Resultantly, RFR outperforms in terms of prediction model fitness and error. The optimization model is run for 6 samples in which the minimum Euclidean distance from component position after reflow process from ideal position (i.e., the center of pads) is outlined as 25.57 ({\mu}m) regarding defined boundaries in model.