A Real-Time Automatic Structural-Loss Detection and Stopping Rule of Semiconductor Single-Crystal-Silicon-Growth <100> and <111>

Abstract

The occurrence of the “structural-loss” defect during single-crystal silicon growth (SCSG) is a significant issue in semiconductor manufacturing. When structural-loss occurs, it signifies a deviation from the desired quality of single-crystal formation, leading to the need to halt the growth process. Currently, there is a lack of scholarly literature addressing the determination of an optimal stopping time to promptly halt the process upon the occurrence of the defect on-line. Our research makes a substantial contribution by addressing this gap in the SCSG process, specifically focusing on orientations <100> and <111>. The study utilizes advanced AI with YOLO-v7 and innovative approaches. These include precise annotation of crystal misorientation features through a comprehensive definition of structural-loss and novel labeling techniques, identification of optimal hyper-parameters through a robust design, and the implementation of effective stopping rule mechanisms. Significant progress has been achieved in decision-making through the implementation of the stoping time shift to terminate the SCSG process within an average of less than 3 minutes for <100> orientations (with a standard error of 0.3 minutes) and less than 5 minutes for <111> orientations (with a standard error of 0.5 minutes). The promising results indicate that the proposed approaches have the capability to substitute manual inspections, opening up possibilities for new perspectives in this particular field.