Paper
2 April 2014 Hybrid metrology universal engine: co-optimization
Alok Vaid, Carmen Osorio, Jamie Tsai, Cornel Bozdog, Matthew Sendelbach, Eyal Grubner, Roy Koret, Shay Wolfling
Author Affiliations +
Abstract
In recent years Hybrid Metrology has emerged as an option for enhancing the performance of existing measurement toolsets and is currently implemented in production1. Hybrid Metrology is the practice to combine measurements from multiple toolset types in order to enable or improve the measurement of one or more critical parameters. While all applications tried before were improved through standard (sequential) hybridization of data from one toolset to another, advances in device architecture, materials and processes made possible to find one case that demanded a much deeper understanding of the physical basis of measurements and simultaneous optimization of data. This paper presents the first such work using the concept of co-optimization based hybridization, where image analysis parameters of CD-SEM (critical dimensions Scanning Electron Microscope) are modulated by profile information from OCD (optical critical dimension – scatterometry) while the OCD extracted profile is concurrently optimized through addition of the CD-SEM CD results. Test vehicle utilized in this work is the 14nm technology node based FinFET High-k/Interfacial layer structure.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alok Vaid, Carmen Osorio, Jamie Tsai, Cornel Bozdog, Matthew Sendelbach, Eyal Grubner, Roy Koret, and Shay Wolfling "Hybrid metrology universal engine: co-optimization", Proc. SPIE 9050, Metrology, Inspection, and Process Control for Microlithography XXVIII, 905009 (2 April 2014); https://doi.org/10.1117/12.2048939
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Cited by 4 scholarly publications.
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KEYWORDS
Semiconducting wafers

Metrology

Image analysis

Critical dimension metrology

Diffractive optical elements

Modulation

Detection and tracking algorithms

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