Integrating Manufacturing Intelligence, Computer Vision, and Process Observation for Yield Improvement and Failure Prediction in Electronics Manufacturing

Abstract

Electronics manufacturing processes are complex and prone to yield loss and latent failures due to subtle process deviations and quality escapes. This paper presents a holistic approach to improving first-pass yield and predicting failures by integrating a Manufacturing Intelligence for Reliability and Automated Insights (MIRAI) data platform with computer vision-based monitoring of Standard Operating Procedure (SOP) adherence. The proposed system combines self-serve data analytics workflows for yield and field failure analysis with real-time process observation using deep learning vision models. Manufacturing data from production tests, reliability screenings, and field returns are aggregated and analyzed to identify key signals correlating with yield drops and field fallouts. Simultaneously, a PROSPECT tool employs AI cameras at assembly stations to record operator actions and detect deviations from standard procedures. A machine learning failure prediction model is then trained on the enriched dataset (including vision-detected deviations) to proactively flag high-risk units in real time.