Enhancing the automated quality inspection in manufacturing process through parameter optimization

Abstract

This manuscript presents the optimization work of vision inspection at the semiconductor industry, focusing on the top view vision inspection. The top view vision inspection includes checking the tip to tip, lead to lead and laser marking of the product. In this work, the focus was on enhancing the potential vision parameter that causes over-rejection. The work started with the identification of the vision parameter that contributes to the over-rejection by the vision system. Three factors have been identified, which are the lead shutter time, laser shutter time and brightness value. All factors were tested using the design expert software. A comprehensive data collection was conducted to gather essential measurements by the vision system. Upon completion of the data collection, the optimization of the parameters was done using the full factorial method. At the end of this work, the optimized parameter setting has been validated using the dedicated machine, and monitoring of the result has been conducted based on the defined timeline. Post the optimized parameter setting, the vision was able to capture measurement value similar to the drawing value within the acceptable tolerance. This work has significantly reduced over-rejection and has indirectly improved the production rate.