In high-throughput electronics manufacturing, process stability and yield critically depend on precise control across all stages of the Surface Mount Technology (SMT) line. However, ongoing trends toward miniaturization introduce new challenges. While recent advancements have deepened our understanding of product quality, a significant knowledge gap remains regarding the influence of process parameters on key quality factors, such as defects, solder joint shapes, and overall yield. In particular, the impact of solder paste deposition quality, chip placement offsets, and the self-alignment behavior of components during reflow is not yet fully understood in relation to the quality factor. To address these challenges, we propose an AI-driven closed-loop control framework for proactive and adaptive parameter management across the SMT process. The framework is designed to predict and optimize critical quality indicators, including process capability indices (e.g., Cpk), solder joint morphology, and first-pass yield. By integrating physics-based insights with data-driven models, the system simulates and controls key process stages in real-time with enhanced interpretability while overcoming data shortage issues. The proposed system leverages in-line inspection data to refine both printing and placement operations. Specifically, it adjusts component placement locations to compensate for solder paste misalignment, thereby enhancing the effectiveness of self-alignment during the reflow process. In addition, the framework employs a Multiphysics-informed, data-driven simulation to fine-tune the reflow oven profile, optimizing parameters such as peak temperature and time above liquidus (TAL) in alignment with manufacturer guidelines. Through this integrated approach, the framework facilitates predictive, adaptive control of SMT operations, enabling improved first-pass yield, reduced rework, and a scalable pathway toward intelligent SMT manufacturing.
