In this paper we proposed a new semiconductor quality monitoring methodology – Process Sensor Log Analysis (PSLA) – using process sensor data for the detection of wafer defectivity and quality monitoring. We developed exclusive key parameter selection algorithm and user friendly system which is able to handle large amount of big data very effectively. Several production wafers were selected and analyzed based on the risk analysis of process driven defects, for example alignment quality of process layers. Thickness of spin-coated material can be measured using PSLA without conventional metrology process. In addition, chip yield impact was verified by matching key parameter changes with electrical die sort (EDS) fail maps at the end of the production step. From this work, we were able to determine that process robustness and product yields could be improved by monitoring the key factors in the process big data.
Younghoon Sohn, Hyun Lee, Yusin Yang, and Chungsam Jun, "A new method for wafer quality monitoring using semiconductor process big data," Proc. SPIE 10145, Metrology, Inspection, and Process Control for Microlithography XXXI, 101450T (Presented at SPIE Advanced Lithography: February 28, 2017; Published: 28 March 2017); https://doi.org/10.1117/12.2256435.
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