Quantifying the Impact of Outdoor Airborne Nano-Contamination on eSiGe Defect Generation and Machine Learning-Based Predictive Modeling

Abstract

A thorough investigation was conducted to determine the impact of outdoor airborne nanoparticles on defect generation during semiconductor manufacturing. Periods of elevated airborne particle levels, along with increased occurrences of embedded Silicon-Germanium (eSiGe) defects, were analyzed using experimental bare wafers designed to capture nanoparticles. Defect counts were analyzed to trace their origins. A novel data processing algorithm was developed to clarify and quantify the relationship between external airborne nanoparticles and defect formation. The findings indicate that eSiGe defect particles attributable to external airborne nano-contamination were generated at rates ranging from 1% to 6%, depending on the fab site. The robustness of the algorithm was validated through the application of an Artificial Neural Network (ANN) technique. Key parameters influencing eSiGe defects, identified as outdoor PM2.5 and Fab particles, were further analyzed using Random Forest Regression (RFG) and Quantile Regression (QR). Additionally, the application of Support Vector Regression (SVR) significantly enhanced the prediction accuracy of eSiGe defect particles, achieving an improvement of approximately 56% compared to RFG modeling. This study uniquely combines short-term experimental methods with long-term inline data science techniques to elucidate the effects of outdoor nanoparticles on eSiGe defects.