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The use of statistically designed experiments for optimizing photoresist processes in manufacturing environments provides a powerful method of establishing a robust process. Unfortunately, there is an abundance of experimental designs, approaches, and analysis techniques available to the experimenter. A sequential approach of experimental design will be outlined which can logically and efficiently take the lithography process engineer from the initial problem objective(s) to the final optimized process. The test
vehicle used to demonstrate this approach is a novel negative tone photoresist which is based on a positive type chemical formulation with a novolac-bound isourea. Transformation of a multivariable response to simultaneously locate optima from seven original design
variables is employed. Minimization of film loss, maximization of process latitude, and optimization of resist resolution to a fixed target are concurrently achieved through manipulation of variables including bake temperatures, exposure energies, and developer normality. With the variables and responses specified, the first stage of the sequential approach is an initial screening
experiment based on an extended Morris Mitchell design with center point replicates. This efficient design minimizes experimental trials while limiting confounding of interactions between 1st and 2nd order
coefficients. Using the technique of steepest ascent to select a region of optimal response values, a second experiment is performed using a factorial design. Only those variables showing significance levels greater than 95% are carried forward from the screening analysis. Finally, a response surface based on a full second order quadratic response surface model (RSM) is demonstrated. Model validity, confidence intervals and significance levels are investigated. Three different process options are derived from the RSM process model and are experimentally verified.
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