Showing papers by "Peter J. Rossky published in 2019"

Device Chemistry of Graphene Transistors

Abstract: Graphene is an attractive material for microelectronics applications, given such favourable electrical characteristics as high mobility, high operating frequency, and good stability. If graphene is to be implemented in electronic devices on a mass scale, then it must be compatible with existing semiconductor industry fabrication processes. Unfortunately, such processing introduces defects and impurities to the graphene, which cause scattering of the charge carriers and changes in doping level. Scattering results in degradation of electrical performance, including lower mobility and Dirac point shifts. In this paper, we review methods by which to mitigate the effects of charged impurities and defects in graphene devices. Using capping layers such as fluoropolymers, statistically significant improvement of mobility, on/off ratio, and Dirac point voltage for graphene FETs have been demonstrated. These effects are also reversible and can be attributed to the presence of highly polar groups in these capping layers such as carbon-fluoride bonds in the fluoropolymer acting to electrostatically screen charged impurities and defects in or near the graphene. In other experiments, graphene FETs were exposed to vapour-phase, polar, organic molecules in an ambient environment. This resulted in significant improvement to electrical characteristics, and the magnitude of improvement to the Dirac point scaled with the dipole moment of the delivered molecule type. The potential profile produced in the plane of the graphene sheet by the impurities was calculated to be significantly reduced by the presence of polar molecules. We present strong evidence that the polar nature of capping layers or polar vapour molecules introduced to the surface of a graphene FET act to mitigate detrimental effects of charged impurities/defects.

Prediction of optical spectra of coarse-grained polymers as a sequence generation problem: the Recurrent Neural Networks solution

Abstract: Coarse-grained simulations of conjugated polymers have become a popular way of investigating the device physics of organic photovoltaics. While UV-Vis spectroscopy remains one of key experimental methods for the interrogation of these devices, a rigorous bridge between coarse-grained simulations and spectroscopy has never been established. Here we address this challenge by developing a method that predicts spectra of conjugated polymers directly from coarse-grained representations while avoiding ad-hoc procedures such as back-mapping from coarse-grained to atomistic representations followed by computing the spectra using standard quantum chemistry methods. Our approach is based on a generative deep learning model: the long-short-term memory recurrent neural network (LSTM-RNN) and it is suggested by the apparent similarity between natural languages and the mathematical structure of the perturbative expansions of the excited state energies due to small fluctuations of the polymer conformation. We use this model to demonstrate a dangerous discrepancy between the spectra obtained in the coarse-grained representation and after the back-mapping. This indicates that standard protocols may require additional fine-tuning in order to become reliable and that our model presents a novel tool uniquely suited for improving the back-mapping protocols and for including spectral data in the development of coarse-grained potentials.