Patterning tool characterization by causal variabilitydecomposition
Crid Yu; Hua-Yu Liu; Spanos, C.J.
Semiconductor Manufacturing, IEEE Transactions on
Volume 9, Issue 4, Nov 1996 Page(s):527 - 535
Digital Object Identifier 10.1109/66.542168
Summary:A spatial and causal classification of process error provides
opportunities for the accurate determination and efficient management of
process error budget. Traditional metrology is posed with this dilemma:
variability sampling requires cheap, highly repeatable metrology, such
as electrical measurements, which also confound error sources of the
variability sampled. In response, statistical metrology has been
proposed as a novel combination of cost-effective metrology with
subsequent statistical or experimental data processing to provide a
technique that is capable of error decomposition into equipment causes.
The methodology, consisting of 1) reticle and experiment design, 2) data
filtering, and 3) error budget formulation, is presented and is general
to a short-loop thin-film patterning sequence. A .35-μm polygate
patterning sequence is chosen to demonstrate this technique. Reticle
design and statistical filtering have been presented in a previous
publication, and are summarized here. The second causal data filter is
presented in this work, Aided by additional experimentation, a physical
filter decomposes the separate contributions and interactions of the
reticle and stepper. A portion of the error budget is calculated,
including the effects of spatial correlation. The results of
decomposition yields a numerical metric for equipment and process
manufacturability. Results are presented that illustrate the use of the
manufacturability metric in equipment selection and process design
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