A Proactive Approach of Optimizing Real-Time Equipment Monitoring Settings for Enhancing End-of-Line Yield

Abstract

In semiconductor manufacturing, wafer acceptance test (WAT) data consists of end-of-line (EOL) electrical parameters reflecting product quality and process capability, while in-line equipment plays a crucial role in shaping these outcomes. Engineers collect real-time monitoring (RTM) data that are used for reactive diagnosis when WAT detects issues. It is highly desirable to have quantitative prediction models linking RTM data to EOL parameters, so that RTM control region settings can be proactively optimized to keep WAT results on target with low variations, ultimately enhancing EOL yield. This paper designs WAPOR, a framework for EOL parameter prediction exploiting significant RTM items and their monitoring setting optimization, to proactively reduce resultant WAT variations. There are three innovations: (i) Key RTM Item Identification (H-RIS) for individual EOL parameters by combining three machine learning methods for both linear and non-linear analysis; (ii) WAT Parameter Prediction Model (WPBM) learned from applying Deep Back-Propagation Neural Networks (DBPN) to multi-dimensional, non-linear prediction of an EOL parameter value based on its key RTM items; and (iii) equipment monitoring control setting optimization (RRS-GA) to make WAT on target with low variation. As such, WAPOR moves beyond traditional linear approaches, uncovers complex relationships and empowers engineers to set RTM parameters proactively to make WAT forecast fall within WAT specification and minimize its variance. Simulation results demonstrate that WAPOR maintains WAT target alignment within 2% of the target while reducing variation by 49%. WAPOR has a good potential to improve process capability and EOL yield.