Full-field feature profile models in process control

Terrence E. Zavecz

doi:10.1117/12.600240

17 May 2005 Full-field feature profile models in process control

Terrence E. Zavecz

Proceedings Volume 5755, Data Analysis and Modeling for Process Control II; (2005) https://doi.org/10.1117/12.600240
Event: Microlithography 2005, 2005, San Jose, California, United States

Abstract

Most process window analysis applications are capable of deriving the functional focus-dose workspace available to any set of device specifications. Previous work in this area has concentrated on calculating the superpositioned optimum operating points of various combinations of feature orientations or feature types. These studies invariably result in an average performance calculation that is biased by the impact of the substrate, reticle and exposure tool contributed perturbations. Many SEM's and optical metrology tools now provide full-feature profile information for multiple points in the exposure field. The inclusion of field spatial information into the process window analysis results in a calculation of greater accuracy and process understanding because now the capabilities of each exposure tool can be individually modeled and optimized. Such an analysis provides the added benefit that after the exposure tool is characterized, it's process perturbations can be removed from the analysis to provide greater understanding of the true process performance. Process window variables are shown to vary significantly across the exposure field of the scanner. Evaluating the depth-of-focus and optimum focus-dose at each point in the exposure field yields additional information on the imaging response of the reticle and scan-linearity of the exposure tool's reticle stage. The optimal focus response of the reticle is then removed from a full wafer exposure and the results are modeled to obtain a true process response and performance.

Citation Download Citation

Terrence E. Zavecz "Full-field feature profile models in process control", Proc. SPIE 5755, Data Analysis and Modeling for Process Control II, (17 May 2005); https://doi.org/10.1117/12.600240

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