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EETE JULAUG 2013

FLEXIBLE ELECTRONICS Predictive modeling approach boosts the development of thin-film organic electronics By Alexander Mityashin, David Beljonne, Jérôme Cornil, Claudio Zannoni, and Paul Heremans Imec (Belgium), University of Bologna (Italy) and University of Mons (Belgium) have developed a unique multi-scale methodology to model the development cycle for thin-film organic electronic materials and devices. This predictive approach has been applied in different case studies demonstrating its practical application. The new methodology will boost the development of organic electronics by providing a strong modeling help to experimental optimization. The need for a new modeling methodology Organic molecules have been exploited in a large variety of electronic and optoelectronic applications, such as organic light-emitting diodes (OLEDs), thin-film transistors (OTFTs), organic solar cells (OPV) and sensors. Several of these applications are currently making their first all-out attempts to enter the market. For many others, however, the technology is not yet there. Today, both the limited electrical performance and the environmental instability of molecular materials require major breakthroughs in their fundamental understanding. Among many strategies to facilitate the technology development, theoretical models are being sought to provide guidelines for material design and device architecture. In this context, we have recently developed a new theoretical methodology for the predictive modeling of organic molecular semiconductors. Compared to existing instruments for molecular modeling, its conceptual novelty consists in integrating several state-of-the-art techniques into one multi-scale workflow. With the pivotal incentive of providing a comprehensive physical outlook, the proposed methodology covers various steps of the development cycle - from chemical design of new organic molecules to their thin-film assembly and electronic properties in devices. Principle of the new modeling approach The scheme of a typical modeling study is shown in figure 1 and includes three major steps. First of all, for every new molecular material that we would like to model, the physicochemical and electronic properties of individual molecules are parameterized by rigorous ab-initio modeling. These properties are essential to understand how these molecules interact together in solids, and how their interactions influence the thin-film morphology, its crystal structure and possible defects. Conjugating this understanding with molecular dynamics modeling, we construct molecular films of several thousands of molecules, with thickness up to a few tens of nanometers. These dimensions are appropriate for characterizing device-relevant electronic processes. In a similar way, one could model molecular deposition and thin-film growth processes, imitating the industrially-relevant fabrication techniques that we like to elucidate. Finally, electronic and electrical properties of the obtained films are revealed by combining micro-electrostatics and charge transport modeling. Micro-electrostatics is first applied to study the energy level landscapes for the charge transport. Later on, these landscapes are populated with mobile charges, identical to those injected from metal electrodes in thin-film devices. The motion of these charges is monitored by the charge transport modeling to reveal their speed and transport efficiencies; these properties are responsible for the material conductivity and charge carrier mobility. To make this thorough integration possible, a certain technical innovation was required. Among many things, the most crucial one was to work out the communication protocols to enable individual methods to “talk” to each other. The smooth matching of characteristic scales, accuracies and computational complexities of different methods is assured by optimized and newly-developed data-exchange protocols. Once such framework is established, the work-flow of figure 1 is only an example of a possible modeling realization; many more techniques can be integrated as new modules, as illustrated in the figure by the stacks of candidate techniques available for every modeling block. Such integration results in a unique configurable platform for predictive modeling from nano-scale of individual molecules to macro-scale of thin-film devices. Revealing the mechanism of molecular doping in organic semiconductors The multi-scale methodology enables new strategies to tackle scientific challenges at scales and complexities that previously were not accessible by conventional modeling techniques or experiments. For example, we have applied this approach to unravel the mechanism of molecular doping in crystalline organic semiconductors. Monitoring the doping-associated electronic processes offered the first assessment of the doping mechanism at the molecular scale and of its efficiency. The main steps of this study are summarized in the top part of figure 1. For the convenience of the experimental validation, we selected a well-known semiconductor-doping combination: p-type semiconductor pentacene and its complementary dopant F4TCNQ. After ab-initio parameterization of individual molecular properties of the host and doping materials, films Alexander Mityashin is a member of the Large Area Electronics department at imec and a PhD candidate at the K.U.Leuven - imec.be – He can be reached at Alexander.Mityashin@imec.be David Beljonne and Jérôme Cornil are Research Directors of the Belgian National Fund for Scientific Research (FNRS) at the University of Mons – They can be reached at David.Beljonne@umons.ac.be and Jerome.Cornil@umons.ac.be, respectively. Claudio Zannoni is Professor of Physical Chemistry at the University of Bologna - Claudio.Zannoni@unibo.it Paul Heremans is Professor in Electronic Engineering at the K.U.Leuven, Fellow at imec and Technology Director at Holst Centre – He can be reached at Paul.Heremans@imec.be 30 Electronic Engineering Times Europe July/August 2013 www.electronics-eetimes.com


EETE JULAUG 2013
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