Real-Time AI-Based Detection and Prevention of Component-Level Failures in Electronic Assembly

Ensuring the reliability of electronic assemblies requires early identification and rejection of defective components before mounting. This study introduces a real time, AI-based inspection method integrated within the pick-and-place process to prevent failure-prone components from entering service. The system leverages high-speed imaging and deep learning algorithms to detect latent defects—such as corrosion, bent leads, metallization loss, and contamination—known to contribute to early-life and field failures. A two-stage architecture allows initial high-sensitivity detection followed by targeted classification, achieving rejection rates of 59 to 207 defects per million (DPM), depending on user-defined quality thresholds. Importantly, manufacturers are provided with a configurable screening interface—a “quality control knob”—enabling real-time trade-offs between detection sensitivity and attrition cost. This adaptive method not only enhances compliance with IPC-A-610 standards but also supports failure prevention strategies tailored to the criticality of each component type. Case studies and defect-specific visual data validate the system’s efficacy in reducing rework, field failures, and long-term reliability risks. This approach represents a significant advancement in proactive failure mitigation in electronics manufacturing.