PADS: Predictive Anomaly Detection for SMT Solder Joints Using Novel Features From SPI and Pre-AOI Data

This research proposes a predictive anomaly detection (AD) framework for solder joints. In surface mount technology (SMT), anomalous solder joints reduce the reliability of printed circuit boards (PCBs), which raises reworking expenses for PCB assembly lines. Therefore, predictive AD is essential to prevent solder joints with anomalies. The solder joint formation consists of three primary phases: solder paste printing, pick and place, and solder reflow. This research aims to predict the solder joint’s quality before the solder reflow phase by a novel framework, predictive AD for solder joints, which is called PADS. PADS first extracts 65 solder-joint-related features from datasets, then learns normal solder joints’ patterns by reconstructing these features, and finally identifies a sample as an anomaly if its reconstruction error exceeds a designated threshold. The uniqueness of PADS is the utilization of novel features generated from interpreting the existing physics-based models and substantial real-world data acquired from SMT inspection machines, i.e., solder paste inspection (SPI) and automatic optical inspection (AOI). PADS has been extensively evaluated with commercial chip resistors R0402M (
$0.4 \times 0.2$
mm), R0603M (
$0.6 \times 0.3$
mm), and R1005M (
$1.0 \times 0.5$
mm), as well as SAC305 solder paste. The experimental results indicate that these novel features enable PADS to perform better in anomaly prediction for solder joints, and PADS outperforms many competitive baselines in prediction accuracy.