scholarly journals Numerical Simulation of a Mechanically Stacked GaAs/Ge Solar Cell

2017 ◽  
Vol 7 (3) ◽  
pp. 1611-1614
Author(s):  
S. Enayat Taghavi Moghaddam ◽  
S. Mehrdad Kankanani
Keyword(s):  
Output Power ◽  
Carrier Density ◽  
Sun Exposure ◽  
Internal Field ◽  
Energy Band Structure ◽  
Dark Condition ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Current Curve ◽  
Sun Light

In this paper, GaAs and Ge solar cells have been studied and simulated separately and the inner characteristics of each have been calculated including the energy band structure, the internal field, carrier density distribution in the equilibrium condition (dark condition) and the voltage-current curve in the sun exposure with the output power of each one. Finally, the output power of these two mechanically stacked cells is achieved. Drift-diffusion model have been used for simulation that solved with numerically method and Gummel algorithm. In this simulation, the final cells exposed to sun light in a standard AM 1.5 G conditions and temperatures are 300° K. The efficiency of the proposed structure is 9.47%. The analytical results are compared with results of numerical simulations and the accuracy of the method used is shown.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

scholarly journals Informing cognitive abstractions through neuroimaging: The neural drift diffusion model.

2015 ◽  
Vol 122 (2) ◽  
pp. 312-336 ◽  
Author(s):  
Brandon M. Turner ◽  
Leendert van Maanen ◽  
Birte U. Forstmann
Keyword(s):  
Diffusion Model ◽  
Drift Diffusion Model ◽  
Drift Diffusion

Incorporating photon recycling into the analytical drift-diffusion model of high efficiency solar cells

2014 ◽  
Vol 116 (19) ◽  
pp. 194504 ◽  
Author(s):  
Matthew P. Lumb ◽  
Myles A. Steiner ◽  
John F. Geisz ◽  
Robert J. Walters
Keyword(s):  
Solar Cells ◽  
Diffusion Model ◽  
High Efficiency ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
High Efficiency Solar Cells ◽  
Photon Recycling

A Discretization Scheme for a Quasi-Hydrodynamic Semiconductor Model

1997 ◽  
Vol 07 (07) ◽  
pp. 935-955 ◽  
Author(s):  
Ansgar Jüngel ◽  
Paola Pietra
Keyword(s):  
Finite Elements ◽  
Mixed Finite Elements ◽  
Exponential Fitting ◽  
Discretization Scheme ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Current Conservation ◽  
Numerical Tests ◽  
Finite Elements Methods ◽  
Degenerate Type

A discretization scheme based on exponential fitting mixed finite elements is developed for the quasi-hydrodynamic (or nonlinear drift–diffusion) model for semiconductors. The diffusion terms are nonlinear and of degenerate type. The presented two-dimensional scheme maintains the good features already shown by the mixed finite elements methods in the discretization of the standard isothermal drift–diffusion equations (mainly, current conservation and good approximation of sharp shapes). Moreover, it deals with the possible formation of vacuum sets. Several numerical tests show the robustness of the method and illustrate the most important novelties of the model.

scholarly journals Speed, Accuracy, and the Optimal Timing of Choices

2018 ◽  
Vol 108 (12) ◽  
pp. 3651-3684 ◽  
Author(s):  
Drew Fudenberg ◽  
Philipp Strack ◽  
Tomasz Strzalecki
Keyword(s):  
Sequential Sampling ◽  
Decision Time ◽  
Optimal Timing ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Prior Beliefs ◽  
Constant Cost ◽  
Speed Accuracy ◽  
Decision Times ◽  
Choice Probabilities

We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices. (JEL C41, D11, D12, D83)

DISCRETIZATION METHOD OF HYDRODYNAMIC EQUATIONS FOR SIMULATION OF GaN MESFETs

2008 ◽  
Vol 22 (16) ◽  
pp. 1599-1608
Author(s):  
M. REZAEE ROKN-ABADI ◽  
H. ARABSHAHI ◽  
M. R. BENAM
Keyword(s):  
Experimental Methods ◽  
Two Dimensions ◽  
Balance Model ◽  
Discretization Method ◽  
Hydrodynamic Equations ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Transport Models ◽  
Wurtzite Phase ◽  
Inertia Effects

A finite discretization method in two dimensions has been developed and used to model electron transport in wurtzite phase GaN MESFETs. The model is based on the solutions of the highly-coupled nonlinear hydrodynamic partial differential equations. These solutions allow us to calculate the electron drift velocity and other device parameters as a function of the applied electric field. This model is able to describe inertia effects which play an increasing role in different fields of micro and optoelectronics where simplified charge transport models like drift-diffusion model and energy balance model are no longer applicable. Results of numerical simulations are shown for a two-dimensional GaN MESFET device which are in fair agreement with other theoretical or experimental methods.

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