An intelligent virtual metrology system with adaptive update for semiconductor manufacturing. (April 2017)

• Record Type: Journal Article
• Authors: Kang, Seokho; Kang, Pilsung
• Is Part Of: Journal of process control. Volume 52(2017)
• Page Start: 66
• Page End: 74
• Publication Date: 2017-04
• Subjects: Virtual metrology -- Semiconductor manufacturing -- Adaptive update -- Reliability estimation; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.02.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: An intelligent virtual metrology system based on adaptive updates is proposed. It integrates prediction, reliability estimation, and model updating processes. Actual metrology is only performed for those wafers with low reliability. The prediction model is instantly updated with actual metrology results. A better trade-off between metrology accuracy and cost is achieved. Abstract: Virtual metrology involves the estimation of metrology values using a prediction model instead of metrological equipment, thereby providing an efficient means for wafer-to-wafer quality control. Because wafer characteristics change over time according to the influence of several factors in the manufacturing process, the prediction model should be suitably updated in view of recent actual metrology results. This gives rise to a trade-off relationship, as more frequent updates result in a higher accuracy for virtual metrology, while also incurring a heavier cost in actual metrology. In this paper, we propose an intelligent virtual metrology system to achieve a superior metrology performance with lower costs. By employing an ensemble of artificial neural networks as the prediction model, the prediction, reliability estimation, and model update are successfully integrated into the proposed virtual metrology system. In this system, actual metrology is only performed for those wafers where the current prediction model cannot perform reliable predictions. When actual metrology is performed, the prediction model is instantly updated to incorporate the results. Consequently, the actual metrology ratio is automatically adjusted according to the corresponding circumstances. We demonstrate the effectiveness of the method through experimental validation on actual datasets.