Block copolymer (BCP) self-assembly is capable of producing well-ordered periodic structures on the nanometer scale when directed by an underlying chemical pattern. These structures and features have many applications, particularly in the semiconductor industry. Typically, the features need to be within a certain level of precision to be used on a wide scale, so probing the full three-dimensional morphology of these BCP thin films is necessary to characterize how different chemical patterns guide and distort these structures.
Alec uses coarse grained BCP Monte Carlo simulations to interpret BCP small angle X-ray scattering (SAXS) data. These simulations rely on the physics of self-assembly to determine which BCP structures correspond to different scattering profiles. In this way, these simulations can be used to extract the difficult to interpret structural information from SAXS data. Since SAXS is noninvasive and probes all three dimensions of a film (rather than just the surface), this technique can be used as an accurate three-dimensional metrology for BCP self-assembly. Furthermore, the simulation thermodynamics can be used to determine the energetic parameters that characterize different BCP systems.
Alec is from Wilsonville, Oregon, a suburb of nearby Portland. He received his BS in Chemical Engineering from Oregon State University in 2014. In September 2014 he joined the PME at the University of Chicago to start a PhD under the direction of Professor Juan de Pablo.
- Electronic structure at coarse-grained resolutions from supervised machine learning
- Structural Correlations and Percolation in Twisted Perylene Diimides Using a Simple Anisotropic Coarse-Grained Model
- Optimizing self-consistent field theory block copolymer models with X-ray metrology
- Derivation of Multiple Covarying Material and Process Parameters Using Physics-Based Modeling of X-ray Data