2018
DOI: 10.1016/j.md.2018.06.002
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Process optimization for microstructure-dependent properties in thin film organic electronics

Abstract: The processing conditions during solvent-based fabrication of thin film organic electronics significantly determine the ensuing microstructure. The microstructure, in turn, is one of the key determinants of device performance. In recent years, one of the foci in organic electronics has been to identify processing conditions for enhanced performance. This has traditionally involved either trial-and-error exploration, or a parametric sweep of a large space of processing conditions, both of which are time and res… Show more

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Cited by 12 publications
(5 citation statements)
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“…have studied the evaporation‐induced phase separation based on the Cahn–Hilliard–Cook equation . Based on these precise morphologies, dynamic Monte Carlo or graph theory–based formulations are used to characterize the electrical properties.…”
Section: Introductionmentioning
confidence: 99%
“…have studied the evaporation‐induced phase separation based on the Cahn–Hilliard–Cook equation . Based on these precise morphologies, dynamic Monte Carlo or graph theory–based formulations are used to characterize the electrical properties.…”
Section: Introductionmentioning
confidence: 99%
“…This question is particularly important to isolate promising processing windows that produce high-performing devices. Promising approaches include surrogate models based on smart sampling, 48 and ideas of manifold learning. 49…”
Section: Discussionmentioning
confidence: 99%
“…Several research groups have used those features to establish SP maps [10,[10][11][12][13][14][15][16] in OPV and leveraged those maps for microstructure-sensitive design [17]. In its current form, GraSPI handles the quantification of two-phase morphology, although all data structures are generalizable to handle multiphase morphologies [18].…”
Section: Motivation and Significancementioning
confidence: 99%
“…to build a surrogate model of performance in OPVs [11] and microstructure optimization for OPVs [17,27], as well as to create a corpus of data for analysis [10][11][12][13][14][15][16]. We have applied the approach to quantify two-and three-phase morphologies and extended it to point cloud data for the analysis of molecular dynamics simulations [20].…”
Section: Impactmentioning
confidence: 99%