Optimizing Hybrid Metrology through a Consistent Multi-Tool Parameter Set and Uncertainty Model

Published

April 14, 2014

Author(s)

Richard M. Silver, Bryan Barnes, Nien F. Zhang, Hui Zhou, Andras Vladar, John S. Villarrubia, Regis J. Kline, Daniel Sunday, Alok Vaid

Abstract

There has been significant interest in hybrid metrology as a novel method for reducing overall measurement uncertainty and optimizing measurement throughput (speed) through rigorous combinations of two or more different measurement techniques into a single result. This approach is essential for advanced 3-D metrology when performing model-based critical dimension measurements. However, a number of fundamental challenges present themselves with regard to consistent noise and measurement uncertainty models across hardware platforms, and the need for a standardized set of model parameters. This is of paramount concern when the various techniques have substantially different models and underlying physics. In this presentation we will work through realistic examples using SEM, CDAFM and Optical CD methods applied to sub-20 nm dense feature sets. We will show reduced measurement uncertainties using hybrid metrology on 15 nm CD features and evaluate approaches to adapt quantitative hybrid metrology into a high volume manufacturing environment.

Proceedings Title

Proceedings of the SPIE

Volume

9050

Conference Dates

February 23-27, 2014

Conference Location

San Jose, CA, US

Conference Title

Metrology, Inspection, and Process Control for Microlithography

Pub Type

Conferences

Download Paper

https://doi.org/10.1117/12.2048225

Local Download

Keywords

optical metrology, electromagnetic simulation, evaluate sensitivities and uncertainties, phase sensitive measurements, through-focus three-dimensional field

Citation

Silver, R. , Barnes, B. , Zhang, N. , Zhou, H. , Vladar, A. , Villarrubia, J. , Kline, R. , Sunday, D. and Vaid, A. (2014), Optimizing Hybrid Metrology through a Consistent Multi-Tool Parameter Set and Uncertainty Model, Proceedings of the SPIE, San Jose, CA, US, [online], https://doi.org/10.1117/12.2048225, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=915846 (Accessed September 19, 2024)