Nested Uncertainties and Hybrid Metrology to Improve Measurement Accuracy

Published

April 18, 2011

Author(s)

Richard M. Silver, Nien F. Zhang, Bryan M. Barnes, Hui Zhou, Jing Qin, Ronald G. Dixson

Abstract

In this paper we present a method to combine measurement techniques that reduce uncertainties and improve measurement throughput. The approach has immediate utility when performing model-based optical critical dimension measurements. When modeling optical measurements a library of curves is assembled through the simulation of a multi-dimensional parameter space. Parametric correlation and measurement noise lead to measurement uncertainty in the fitting process resulting in fundamental limitations due to parametric correlations. We provide a strategy to decouple parametric correlation and reduce measurement uncertainties. We also develop the rigorous underlying Bayesian statistical model to apply this methodology to OCD metrology. These statistical methods use a priori information rigorously to reduce measurement uncertainty, improve throughput and develop an improved foundation for comprehensive reference metrology

Proceedings Title

Metrology Inspection and Process Control

Volume

7971

Conference Dates

February 27-March 3, 2011

Conference Location

San Jose, CA

Pub Type

Conferences

Keywords

uncertainties, multi-dimensional parameter space, Hybrid metrology, Bayesian statistical mode, optics

Citation

Silver, R. , Zhang, N. , Barnes, B. , Zhou, H. , Qin, J. and Dixson, R. (2011), Nested Uncertainties and Hybrid Metrology to Improve Measurement Accuracy, Metrology Inspection and Process Control, San Jose, CA (Accessed April 18, 2024)