Campus Units
Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
7-3-2018
Journal or Book Title
Materials Discovery
DOI
10.1016/j.md.2018.06.002
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 resource intensive. This is especially the case when the process → structure and structure → property simulators are computationally expensive to evaluate.
In this work, we integrate an adaptive-sampling based, gradient-free, Bayesian optimization routine with a phase-field morphology evolution framework that models solvent-based fabrication of thin film polymer blends (process → structure simulator) and a graph-based morphology characterization framework that evaluates the photovoltaic performance of a given morphology (structure → property simulator). The Bayesian optimization routine adaptively adjusts the processing parameters to rapidly identify optimal processing configurations, thus reducing the computational effort in process → structure → property explorations. This serves as a modular, parallel ‘wrapper’ framework that facilitates swapping-in other process simulators and device simulators for general process → structure → property optimization. We showcase this framework by identifying two processing parameters, the solvent evaporation rate and the substrate patterning wavelength, in a model system that results in a device with enhanced photovoltaic performance evaluated as the short-circuit current of the device. The methodology presented here provides a modular, scalable and extensible approach towards the rational design of tailored microstructures with enhanced functionalities.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Copyright Owner
Elsevier Ltd.
Copyright Date
2018
Language
en
File Format
application/pdf
Recommended Citation
Pfeifer, Spencer; Pokuri, Balaji Sesha Sarath; Du, Pengfei; and Ganapathysubramanian, Baskar, "Process optimization for microstructure-dependent properties in thin film organic electronics" (2018). Mechanical Engineering Publications. 286.
https://lib.dr.iastate.edu/me_pubs/286
Included in
Electrical and Electronics Commons, Electro-Mechanical Systems Commons, Electronic Devices and Semiconductor Manufacturing Commons, Polymer and Organic Materials Commons
Comments
This is a manuscript of an article published as Pfeifer, Spencer, Balaji Sesha Sarath Pokuri, Pengfei Du, and Baskar Ganapathysubramanian. "Process optimization for microstructure-dependent properties in thin film organic electronics." Materials Discovery (2018). doi: 10.1016/j.md.2018.06.002. Posted with permission.