A Long-term View of Research Targets in Nanoelectronics

Cavin, Ralph K.; Zhirnov, V.V.; Bourianoff, George I.; Hutchby, J.A.; Herr, Daniel J. C.; Hosack, H.H.; Joyner, William H.; Wooldridge, Theresa A.

doi:10.1007/s11051-005-7528-0

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…46 Moreover, phenomena occurring in complex polymer materials are typical multiscale problems. 47 A suitable multiscale modeling tool therefore should be able to treat several processes at different levels of description simultaneously within one single simulation. In case of OPV applications the important length scales range from the quantum-mechanical level, such as the description of absorption processes of photons in nanophase-separated block-copolymer matrices, up to the mesoscopic level of description, in which millions of atoms of the polymer matrix determine the nanostructure and, thus, the performance of the device.…”

Section: Methods and Simulation Detailsmentioning

confidence: 99%

“…[49][50][51] However, we can safely predict that numerous polymer solar cell devices will only hardly be treatable within the conventional particle description, due to the large system sizes and long chain lengths encountered in most polymer applications. 47 To study the influence of defects on the solar cell performance, we developed a new simulation algorithm, which makes use of both either the time-dependent GinzburgLandau (TDGL) method or the self-consistent field theory (SCFT) method, 52 to generate the nanoscale morphology of the polymer blend, in conjunction with the dynamic Monte Carlo method, to mimic the elementary photovoltaic processes. To obtain morphologies of different interfacial lengths, we describe the non-equilibrium dynamics of the phase-separation process using the TDGL method.…”

Section: Methods and Simulation Detailsmentioning

confidence: 99%

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A new multiscale modeling method for simulating the loss processes in polymer solar cell nanodevices

Pershin

Donets

Baeurle

2012

The Journal of Chemical Physics

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The photoelectric power conversion efficiency of polymer solar cells is till now, compared to conventional inorganic solar cells, still relatively low with maximum values ranging from 7% to 8%. This essentially relates to the existence of exciton and charge carrier loss phenomena, reducing the performance of polymer solar cells significantly. In this paper we introduce a new computer simulation technique, which permits to explore the causes of the occurrence of such phenomena at the nanoscale and to design new photovoltaic materials with optimized opto-electronic properties. Our approach consists in coupling a mesoscopic field-theoretic method with a suitable dynamic Monte Carlo algorithm, to model the elementary photovoltaic processes. Using this algorithm, we investigate the influence of structural characteristics and different device conditions on the exciton generation and charge transport efficiencies in case of a novel nanostructured polymer blend. More specifically, we find that the disjunction of continuous percolation paths leads to the creation of dead ends, resulting in charge carrier losses through charge recombination. Moreover, we observe that defects are characterized by a low exciton dissociation efficiency due to a high charge accumulation, counteracting the charge generation process. From these observations, we conclude that both the charge carrier loss and the exciton loss phenomena lead to a dramatic decrease in the internal quantum efficiency. Finally, by analyzing the photovoltaic behavior of the nanostructures under different circuit conditions, we demonstrate that charge injection significantly determines the impact of the defects on the solar cell performance.

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Section: Methods and Simulation Detailsmentioning

confidence: 99%

Section: Methods and Simulation Detailsmentioning

confidence: 99%

A new multiscale modeling method for simulating the loss processes in polymer solar cell nanodevices

Pershin

Donets

Baeurle

2012

The Journal of Chemical Physics

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“…In searching for the new nanoscale devices [4] that can be integrated, looking for alternatives beyond CMOS technology, and decreasing significantly the power dissipation, spin torque oscillator (STO) devices have significant potential [5]. The question is: can they be integrated and can they solve some significant problems with a physically realizable architecture?…”

Section: Computing Via Synchronizationmentioning

confidence: 99%

Spatial-temporal event detection via frameless cellular wave computing - a review

Horváth

Koller

Stubendek

et al. 2014

NOLTA

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The great majority of currently used computational models and devices are using discrete computation. Discrete in time, in value , in parameters. In nature, on the contrary, like in the mammalian retina, difficult tasks are solved with simplicity, low power and elegance, in a wavelike manner, without discretization. Spatial-temporal event detection is a prototype problem in many sophisticated problems of recognition, identification, associative memory, machine-vision, multimodal sensory systems, navigation, and control. In this paper we introduce how continuous spatial-temporal dynamics can be used as algorithmic tools for computation and show a few examples about their usage in practice. These techniques introduce a new kind of thinking also in the field on nonlinear dynamical systems, in general.

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“…In searching for the new nanoscale devices [1] that can be integrated, looking for alternatives beyond CMOS technology, and decreasing significantly the power dissipation, spin torque oscillator (STO) devices seem as outstanding candidates [2]. The question is: can they be integrated and can they solve some significant problems with a physically realizable architecture..…”

Section: Introductionmentioning

confidence: 99%

“…Unlike in earlier mainstream CNN applications, here the cells are oscillators, actually, the STOs. Hence, we will use a one dimensional oscillatory CNN with a couple of rows (1)(2)(3).…”

Section: Introductionmentioning

confidence: 99%

An Associative Memory with oscillatory CNN arrays using spin torque oscillator cells and spin-wave interactions architecture and End-to-end Simulator

Roska

Horváth

Stubendek

et al. 2012

2012 13th International Workshop on Cellular Nanoscale Networks and Their Applications

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An Associative Memory is built by three consecutive components: (1) a CMOS preprocessing unit generating input feature vectors from picture inputs, (2) an AM cluster generating signature outputs composed of spintronic oscillator (STO) cells and local spin-wave interactions, as an oscillatory CNN (O-CNN) array unit, applied several times arranged in space, and (3) a classification unit (CMOS). The end to end design of the preprocessing unit, the interacting O-CNN arrays, and the classification unit is embedded in a learning and optimization procedure where the geometric distances between the STOs in the O-CNN arrays play a crucial role. The O-CNN array has an input vector as a 1D array of oscillator frequencies, and the synchronized O-CNN array codes the output as the phases of the output 1D array. The typical O-CNN array has 1-3 rows of STOs. Simplified STO and interaction macro models are used. A typical example is shown using an End-to-end Simulator.

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A Long-term View of Research Targets in Nanoelectronics

Cited by 14 publications

References 8 publications

A new multiscale modeling method for simulating the loss processes in polymer solar cell nanodevices

A new multiscale modeling method for simulating the loss processes in polymer solar cell nanodevices

Spatial-temporal event detection via frameless cellular wave computing - a review

An Associative Memory with oscillatory CNN arrays using spin torque oscillator cells and spin-wave interactions architecture and End-to-end Simulator

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