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Proceedings Paper

Scanner overlay mix and match matrix generation: capturing all sources of variation
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Paper Abstract

Although lithography equipment and alignment capabilities have evolved significantly since the early stepper days of the 1980’s, the techniques for generating overlay mix and match matrices have remained virtually unchanged. The underlying assumption for traditional mix and match matrices is that the lens signature is the dominant influence in total overlay, and that metrology errors need to be averaged out of the raw data. As step and scan systems were introduced in mid 1990s, improved lens quality has reduced the lens signature errors significantly. However, improvements in stepping accuracy and precision did not keep pace with the rapid reduction in lens distortions. As a result, lens distortion signatures, combined with stepping and scanning repeatability issues, render the traditional “lens distortion matrix” methods for generating mix and match matrices invalid. In this paper several metrology sampling layouts were generated, and demonstrated that with appropriate sampling across known degrees of freedom, it is possible to create a mix and match matrix and modeling more appropriate for 65 nm node alignment tolerances. The mix and match approach captures worst case overlay errors in the matching matrix, and also identifies the root causes of the mix and match error sources between scanners.

Paper Details

Date Published: 24 May 2004
PDF: 12 pages
Proc. SPIE 5375, Metrology, Inspection, and Process Control for Microlithography XVIII, (24 May 2004); doi: 10.1117/12.534359
Show Author Affiliations
Stephen J. DeMoor, Texas Instruments, Inc. (United States)
Jay M. Brown, Nikon Precision, Inc. (United States)
John C. Robinson, KLA-Tencor Corp. (United States)
Simon Chang, Texas Instruments, Inc. (United States)
Colin Tan, Texas Instruments, Inc. (United States)


Published in SPIE Proceedings Vol. 5375:
Metrology, Inspection, and Process Control for Microlithography XVIII
Richard M. Silver, Editor(s)

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