4.6 Article

Quantitative modeling of in situ x-ray reflectivity during organic molecule thin film growth

Journal

PHYSICAL REVIEW B
Volume 84, Issue 7, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.84.075479

Keywords

-

Funding

  1. Cornell Center for Materials Research [NSF-DMR-0520404]
  2. National Science Foundation Materials Research Science and Engineering Center [NSF-DMR-0520404]
  3. National Science Foundation
  4. NIH-NIGMS [NSF-DMR-0225180]
  5. [NSF-ECS-0210693]
  6. [NSF-ECS-0304483]

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Synchrotron-based x-ray reflectivity is increasingly employed as an in situ probe of surface morphology during thin film growth, but complete interpretation of the results requires modeling the growth process. Many models have been developed and employed for this purpose, yet no detailed, comparative studies of their scope and accuracy exists in the literature. Using experimental data obtained from hyperthermal deposition of pentane and diindenoperylene (DIP) on SiO(2), we compare and contrast three such models, both with each other and with detailed characterization of the surface morphology using ex situ atomic force microscopy (AFM). These two systems each exhibit particular phenomena of broader interest: Pentacene/SiO(2) exhibits a rapid transition from rough to smooth growth; DIP/SiO(2), under the conditions employed here, exhibits growth rate acceleration due to a different sticking probability between the substrate and film. In general, independent of which model is used, we find good agreement between the surface morphology obtained from fits to the in situ x-ray data with the actual morphology at early times. This agreement deteriorates at later times, once the root-mean squared (rms) film roughness exceeds about 1 ml. Because layer coverages are under-determined by the evolution of a single point on the reflectivity curve, we also find that the best fits to reflectivity data-corresponding to the lowest values of chi(2)(nu)-do not necessarily yield the best agreement between simulated and measured surface morphologies. Instead, it appears critical that the model reproduces all local extrema in the data. In addition to showing that layer morphologies can be extracted from a minimal set of data, the methodology established here provides a basis for improving models of multilayer growth by comparison to real systems.

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