The bulk of Stein's research will involve performing very simple experiments with chemically amplified resists, interpreting the results of those experiments, and then building models to predict how those same materials will perform under much more complex circumstances, such as at the industrial scale. Having such a model in place would be a homerun for the semiconductor industry, as the time needed to evaluate materials and optimize their processing would be vastly reduced.
This latest study is a spinoff of previous research Stein published in the Journal of Physical Chemistry C in 2012, in which her team outlined a very simple model that can be used to predict a broad range of experimental data. "To our knowledge, that had never been done before," Stein said. "Now we're trying to understand why that model works so well."
Luckily, Stein said she and her team already have some predictions as to why such a simple model might work so well in accurately predicting complex chemical reactions. The general hypothesis, Stein explained, is that their simple model reflects three important factors that she and her research team believe to play a role in determining the outcome of these chemical reactions.
The first factor, Stein explained, relates to the complex dynamics of the polymer itself that, when blended with a catalyst, affects the way the catalyst moves and, therefore, how the reaction moves.
The second factor supposes that the temperature might not be constant thro
|Contact: Audrey Grayson|
University of Houston