Leveraging Machine Signals for Device-Level Quality Detection and Automatic Root Cause Analysis in Semiconductor Wire Bonding

This paper focuses on leveraging machine signal data from wire bond machines by building a data-driven solution to enhance root cause analysis efficiency and real-time quality control in semiconductor wire bonding. Traditional root cause analysis is time-consuming, labor intensive and performed in hindsight. We performed experiments at NXP Semiconductors N.V. that mimicked wire bonding problems caused by forming gas, and subsequently used the resulting real-world data to overcome the traditional root cause analysis challenges. We show that random forest classification models can successfully differentiate between standard and problematic wire bond manufacturing conditions, identifying significant machine-signal-related features associated with forming gas issues. The study demonstrates the effectiveness of linking machine-signal-related features to root causes, enabling proactive detection of potential failures during wire bonding.