| Author(s): |
Richard M. Silver; Nien F. Zhang; Bryan M. Barnes; Hui Zhou; Jing Qin; Ronald G. Dixson; |
| Title: |
Nested Uncertainties and Hybrid Metrology to Improve Measurement Accuracy |
| Published: |
April 18, 2011 |
| 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: |
Metrology Inspection and Process Control |
| Volume: |
7971 |
| Pages: |
pp. 797116-1 - 797116-11 |
| Location: |
San Jose, CA |
| Dates: |
February 27-March 3, 2011 |
| Keywords: |
uncertainties, multi-dimensional parameter space, Hybrid metrology, Bayesian statistical mode, optics |
| Research Areas: |
Optical Physics, Theoretical Computation and Modeling |
