Artificial intelligence-based sampling planning system for dynamic manufacturing process
The dynamic sampling instead of static sampling can more effectively utilize the inspection capacity for quicker excursion detection and increase the throughput of inspection machines without affecting the quality of inspection, so that achieve cycle time reduction. Accordingly, many researchers and semiconductor fabs start investigating the impacts of using dynamic sampling and there is currently a growing need for the dynamic sampling strategies in today's highly competitive semiconductor industry. Meanwhile, the use of artificial intelligence (AI) for knowledge discovery has become more common in industrial and manufacturing process control systems and recent advances in technology, particularly in networking, and information processing, have made the implementation of dynamic process sampling feasible. In this paper the optimal dynamic sampling method and the associated decision process based on AI technique are proposed and the effectiveness of them is validated through actual data sets collected from a semiconductor fabrication line. Finally, we present an AI-based dynamic sampling planning system incorporated the proposed methodology, which possesses four sub-components: wafer bin map (WBM) data mart, optimal sampling method generator (OSMG), sampling knowledge, and sampling adaptation monitor. Our research results provide an effective solution to implement a successful dynamic process sampling.
artificial intelligence manufacturing process control system dynamic sampling planning system