Patterning tool characterization by causal variability decomposition

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