Research on load-balancing scheduling optimization for wafer surface defect detection

The increasing wafer yield and shrinking size pose challenges for real-time defect inspection using a single computer. To address this, we propose enhancing real-time performance by increasing computing devices. However, uneven load distribution due to device performance or defects variations can reduce inspection efficiency. We introduce computer cluster load balancing to solve this, utilizing a load-balancing model and an objective function. We propose the adaptive discrete quantum particle swarm optimization algorithm (ADQPSO) for efficient load balancing and implement the adaptive dynamic smooth weighted round-robin algorithm based on ADQPSO. Experimental results demonstrate that our algorithm achieves the fastest execution speed and up to a 24% improvement in performance. Our approach significantly improves real-time performance and efficiency in wafer surface defect inspection.