Presentation + Paper
27 April 2023 Full wafer OCD metrology: increasing sampling rate without the cost of ownership penalty
Author Affiliations +
Abstract
Optical Critical Dimension (OCD) spectroscopy is a reliable, non-destructive, and high-throughput measurement technique for metrology and process control that is widely used in semiconductor fabrication facilities (fabs). Wafers are sampled sparsely in-line, and measured at about 10-20 predetermined locations, to extract geometrical parameters of interest. Traditionally, these parameters were deduced by solving Maxwell’s equations for the specific film stack geometry. Recently advanced machine learning (ML) models, or combinations of ML and geometric models, has become increasingly attractive due to the several advantages of this approach. Advanced node processes can benefit from more extensive data sampling, but this conflicts with measurement cycle time goals and overall metrology tool costs, which cause fabs to use sparse sampling schemes. In this paper, we introduce a novel methodology that allows wafers to be sampled sparsely but provides the parameters of interest as if they were densely measured. We show how such a methodology allows us to increase data output with no impact on overall measurement time, while maintaining high accuracy and robustness. Such a capability has potentially far-reaching implications for improved process control and faster yield learning in semiconductor process development.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Doutt, Ping-ju Chen, Bhargava Ravoori, Tuyen K. Tran, Eitan Rothstein, Nir Kampel, Lilach Tamam, Effi Aboody, Avron Ger, and Harindra Vedala "Full wafer OCD metrology: increasing sampling rate without the cost of ownership penalty", Proc. SPIE 12496, Metrology, Inspection, and Process Control XXXVII, 124960B (27 April 2023); https://doi.org/10.1117/12.2657471
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KEYWORDS
Semiconducting wafers

Education and training

Etching

Chemical mechanical planarization

Metrology

Machine learning

Data modeling

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