28 March 2017 Computational overlay metrology with adaptive data analytics
Author Affiliations +
With photolithography as the fundamental patterning step in the modern nanofabrication process, every wafer within a semiconductor fab will pass through a lithographic apparatus multiple times. With more than 20,000 sensors producing more than 700GB of data per day across multiple subsystems, the combination of a light source and lithographic apparatus provide a massive amount of information for data analytics. This paper outlines how data analysis tools and techniques that extend insight into data that traditionally had been considered unmanageably large, known as adaptive analytics, can be used to show how data collected before the wafer is exposed can be used to detect small process dependent wafer-towafer changes in overlay.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Emil Schmitt-Weaver, Venky Subramony, Zakir Ullah, Masazumi Matsunobu, Ravin Somasundaram, Joel Thomas, Linmiao Zhang, Klaus Thul, Kaustuve Bhattacharyya, Ronald Goossens, Cees Lambregts, Wim Tel, Chris de Ruiter, "Computational overlay metrology with adaptive data analytics", Proc. SPIE 10145, Metrology, Inspection, and Process Control for Microlithography XXXI, 101450V (28 March 2017); doi: 10.1117/12.2258039; https://doi.org/10.1117/12.2258039

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