scholarly journals Combined Electromagnetic and Drift Diffusion Models for Microwave Semiconductor Device

2011 ◽  
Vol 03 (10) ◽  
pp. 423-429 ◽  
Author(s):  
Samir Labiod ◽  
Saida Latreche ◽  
Mourad Bella ◽  
Christian Gontrand
Keyword(s):  
Semiconductor Device ◽  
Diffusion Models ◽  
Drift Diffusion

Quantum-corrected drift-diffusion models for transport in semiconductor devices

2005 ◽  
Vol 204 (2) ◽  
pp. 533-561 ◽  
Author(s):  
Carlo de Falco ◽  
Emilio Gatti ◽  
Andrea L. Lacaita ◽  
Riccardo Sacco
Keyword(s):  
Semiconductor Devices ◽  
Diffusion Models ◽  
Drift Diffusion

scholarly journals Quantum Energy-Transport and Drift-Diffusion Models

2005 ◽  
Vol 118 (3-4) ◽  
pp. 625-667 ◽  
Author(s):  
Pierre Degond ◽  
Florian M�hats ◽  
Christian Ringhofer
Keyword(s):  
Energy Transport ◽  
Diffusion Models ◽  
Drift Diffusion ◽  
Quantum Energy

scholarly journals Improving parameter recovery for conflict drift-diffusion models

2020 ◽  
Vol 52 (5) ◽  
pp. 1848-1866
Author(s):  
Ronald Hübner ◽  
Thomas Pelzer
Keyword(s):  
Goodness Of Fit ◽  
Drift Rate ◽  
Complex Model ◽  
Diffusion Models ◽  
Necessary Condition ◽  
Grid Search ◽  
Drift Diffusion ◽  
Specific Criterion ◽  
Parameter Recovery ◽  
Parameter Values

Abstract Several drift-diffusion models have been developed to account for the performance in conflict tasks. Although a common characteristic of these models is that the drift rate changes within a trial, their architecture is rather different. Comparative studies usually examine which model fits the data best. However, a good fit does not guarantee good parameter recovery, which is a necessary condition for a valid interpretation of any fit. A recent simulation study revealed that recovery performance varies largely between models and individual parameters. Moreover, recovery was generally not very impressive. Therefore, the aim of the present study was to introduce and test an improved fit procedure. It is based on a grid search for determining the initial parameter values and on a specific criterion for assessing the goodness of fit. Simulations show that not only the fit performance but also parameter recovery improved substantially by applying this procedure, compared to the standard one. The improvement was largest for the most complex model.

Numerical Modeling of Heat Transfer and Phase Transition in Programming the Ovonic Unified Memory Cells

2005 ◽  
Author(s):  
Evan Small ◽  
Sadegh M. Sadeghipour ◽  
Mehdi Asheghi
Keyword(s):  
Heat Transfer ◽  
Phase Transition ◽  
Diffusion Equation ◽  
Dynamic Range ◽  
Semiconductor Device ◽  
Computer Code ◽  
Drift Diffusion ◽  
Thermally Induced ◽  
Design And Optimization ◽  
Phase Prediction

An Ovonic Unified Memory (OUM) cell is a semiconductor device that stores data by a thermally induced phase transition between polycrystalline (set) and amorphous (reset) states in a thin film of chalcogenide alloy. The small volume of active media acts as a programmable resistor switching between a high (amorphous) and low (crystalline) resistance state. The change in the film resistivity (>40X dynamic range) caused by this rapid, reversible structural change is measured to detect the state of the cell (set or reset) for read out. OUM can benefit from a simulator capable of predicting the electrical, thermal, and crystallization behavior for design and optimization, particularly at the present stage of the development. This paper reports on the efforts being made to prepare such a numerical simulator, using an existing finite element computer code as the source for thermal and electrical modeling, and a custom crystallization code for phase prediction. Heat generation in the device is by Joule heating and is achieved by passage of the electric current, which is obtained from the electrical simulation. This result appears in the heat source term of the heat transfer equation that is solved for thermal modeling. As the first attempt the Ohmic current-voltage relation was implemented successfully to simulate set and reset in a two dimensional model of OUM. Solution of the drift-diffusion equation is now underway to capture the semiconductor behavior of the I-V curve. A good progress is made however, still more works needs to be done to fully implement the drift diffusion equation.

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