Relevant bibliographies by topics / Drift-diffusion model

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  2. Dissertations / Theses
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Academic literature on the topic 'Drift-diffusion model'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Drift-diffusion model"

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Fudenberg, Drew, Whitney Newey, Philipp Strack, and Tomasz Strzalecki. "Testing the drift-diffusion model." Proceedings of the National Academy of Sciences 117, no. 52 (2020): 33141–48. http://dx.doi.org/10.1073/pnas.2011446117.

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The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.
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Goncharenko, M., and L. Khilkova. "Homogenized Model of Non-Stationary Diffusion in Porous Media with the Drift." Zurnal matematiceskoj fiziki, analiza, geometrii 13, no. 2 (2017): 154–72. http://dx.doi.org/10.15407/mag13.02.154.

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Fisher, Geoffrey. "A multiattribute attentional drift diffusion model." Organizational Behavior and Human Decision Processes 165 (July 2021): 167–82. http://dx.doi.org/10.1016/j.obhdp.2021.04.004.

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Chen, Xiu Qing, and Li Chen. "The bipolar quantum drift-diffusion model." Acta Mathematica Sinica, English Series 25, no. 4 (2009): 617–38. http://dx.doi.org/10.1007/s10114-009-7171-2.

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Hosseini, Seyed Ebrahim, Rahim Faez, and Hadi Sadoghi Yazdi. "Quantum Corrections in the Drift-Diffusion Model." Japanese Journal of Applied Physics 46, no. 11 (2007): 7247–50. http://dx.doi.org/10.1143/jjap.46.7247.

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Acharyya, Aritra, Subhashri Chatterjee, Jayabrata Goswami, Suranjana Banerjee, and J. P. Banerjee. "Quantum drift-diffusion model for IMPATT devices." Journal of Computational Electronics 13, no. 3 (2014): 739–52. http://dx.doi.org/10.1007/s10825-014-0595-7.

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Ben Abdallah, N., and A. Unterreiter. "On the stationary quantum drift-diffusion model." Zeitschrift für angewandte Mathematik und Physik 49, no. 2 (1998): 251. http://dx.doi.org/10.1007/s000330050218.

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Stoiljković, V., M. J. Howes, and V. Postoyalko. "Nonisothermal drift‐diffusion model of avalanche diodes." Journal of Applied Physics 72, no. 11 (1992): 5493–95. http://dx.doi.org/10.1063/1.351943.

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Li, Xiusheng, Lin’an Yang, and Xiaohua Ma. "Comparison of drift–diffusion model and hydrodynamic carrier transport model for simulation of GaN-based IMPATT diodes." Modern Physics Letters B 33, no. 13 (2019): 1950156. http://dx.doi.org/10.1142/s0217984919501562.

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This paper presents a numerical simulation of a Wurtzite-GaN-based IMPATT diode operating at the low-end frequency of terahertz range. Conventional classical drift–diffusion model is independent of the energy relaxation effect at high electric field. However, in this paper, a hydrodynamic carrier transport model including a new energy-based impact ionization model is used to investigate the dc and high-frequency characteristics of an IMPATT diode with a traditional drift–diffusion model as comparison. Simulation results show that the maximum rf power density and the dc-to-rf conversion efficiency are larger for conventional drift–diffusion model because it overestimates the impact ionization rate. Through hydrodynamic simulation we revealed that the impact ionization rates are seriously affected by the high and rapidly varied electric field and the electron energy relaxation effect, which lead to the rf output power density and the dc-to-rf conversion efficiency falls gradually, and a wider operation frequency band is obtained compared with the drift–diffusion model simulation at frequencies over 310 GHz.
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Liang, Jin. "On a Nonlinear Integrodifferential Drift-Diffusion Semiconductor Model." SIAM Journal on Mathematical Analysis 25, no. 5 (1994): 1375–92. http://dx.doi.org/10.1137/s0036141092238266.

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Dissertations / Theses on the topic "Drift-diffusion model"

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Luzardo, A. "The Rescorla-Wagner Drift-Diffusion model." Thesis, City, University of London, 2018. http://openaccess.city.ac.uk/19210/.

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Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this thesis we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 11 experimental phenomena and show that it can provide an adequate account for 9, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSCTD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used.
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Fard, Pouyan R., Hame Park, Andrej Warkentin, Stefan J. Kiebel, and Sebastian Bitzer. "A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-230313.

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Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments.
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Schmithüsen, Bernhard. "Grid adaptation for the stationary two-dimensional drift-diffusion model in semiconductor device simulation /." Zürich : [s.n.], 2002. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14449.

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Dykhuis, Andrew Frederic. "Capturing irradiation-enhanced corrosion of zircaloy-4 with a charge-based diffusion/drift phase field model." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119029.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 389-400).<br>Zircaloy-4 has been used in pressurized water reactors (PWRs) for decades, and enhanced corrosion rates in reactors compared to out-of-pile have long been observed. However, the exact mechanism explaining the early departure from autoclave kinetics after 3-5 microns of oxide have formed has proved elusive. This thesis considers and evaluates a number of possible explanations for this early acceleration in kinetics. The bulk of the evidence points to Fe depletion from secondary phase particles (SPPs) as the culprit in enhancing Zircaloy-4 corrosion rates in PWRs. These new findings have been incorporated in a mechanistic finite-element phase field model of Zircaloy-4 corrosion called HOGNOSE. It accounts for both diffusion-and drift-based oxygen anion transport in Zircaloy-4 by including the effects of radiation-induced evolution of SPPs in changing the contribution of a local charge transport inequality through their depletion and release of iron. By addressing the imbalance in charged particle transport, the code can be adapted to model multiple zirconium-based alloys in autoclave and irradiated conditions with minimal parameter fitting. Rather than the typical empirical approach, HOGNOSE uses a physics-based methodology to capture the early agreement between autoclave and in-reactor data and the point at which reactor kinetics are enhanced compared to autoclave kinetics. HOGNOSE results agree fairly well with those observed in experiments for oxide thicknesses less than 10 microns, above which other enhancement mechanisms can no longer be safely ignored. HOGNOSE captures increasing amorphization with decreasing temperature, and more subtle corrosion rate enhancement at higher temperatures. Comparisons between HOGNOSE results and literature data suggest that the next focus for mechanistic modeling should consider additional neutron flux effects. To support HOGNOSE development, corrosion testing of Zircaloy-4 in steam at atmospheric pressure and 415 degrees Celsius was performed. Samples were analyzed using a focused ion beam/scanning electron microscope (FIB/SEM) to obtain oxide thickness measurements with greater temporal resolution than is widely provided by autoclave testing. Oxide thickness data was used to determine the thermal dependence of oxygen diffusivity in the oxide within HOGNOSE. HOGNOSE would also benefit from measurements of the concentrations and charge states of cation dopants in post-irradiated Zircaloy oxides to help determine whether this model is truly accurate in its physical description.<br>by Andrew Frederic Dykhuis.<br>Ph. D.
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Sonehag, Christian. "Modeling of Ion Injection in Oil-Pressboard Insulation Systems." Thesis, Uppsala universitet, Fasta tillståndets elektronik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-177600.

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To make a High Voltage Direct Current (HVDC) transmission more energy efficient, the voltage of the system has to be increased. To allow for that the components of the system must be constructed to handle the increases AC and DC stresses that this leads to. One key component in such a transmission is the HVDC converter transformer. The insulation system of the transformer usually consists of oil and oil-impregnated pressboard. Modeling of the electric DC field in the insulation system is currently done with the ion drift diffusion model, which takes into account the transport and generation of charges in the oil and the pressboard. The model is however lacking a description of how charges are being injected from the electrodes and the oil-pressboard interfaces. The task of this thesis work was to develop and implement a model for this which improves the result of the ion drift diffusion model. A theoretical study of ion injection was first carried out and proceeding from this, a model for the ion injection was formulated. By using experimental data from 5 different test geometries, the injection model could be validated and appropriate parameter values of the model could be determined. By using COMSOL Multiphysics®, the ion drift diffusion model with the injection model could be simulated for the different test geometries. The ion injection gave a substantial improvement of the ion drift diffusion model. The positive injection from electrodes into oil was found to be in the range 0.3-0.6 while the negative injection was 0.3 lower. Determination of the parameters for the injection from oil-pressboard interfaces proved to be difficult, but setting the parameters in the range 0.01-1 allowed for a good agreement with the experimental data. Here, a fit could be obtained for multiple assumptions about the set of active injection parameters. Finally it is recommended that the investigation of the ion injection continues in order to further improve the model and more accurately determine the parameters of it. Suggestions on how this work could be carried out are given in the end.
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Sales, Michael F. "Context Dependent Numerosity Representations in Children." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1557146188226533.

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Kaphle, VIkash. "Organic Electrochemical Transistors." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1576594504410991.

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Engelbrecht, Nicholas Eugéne. "On the development and applications of a three-dimensional ab initio cosmic-ray modulation model / Nicholas Eugéne Engelbrecht." Thesis, North-West University, 2012. http://hdl.handle.net/10394/8735.

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A proper understanding of the effects of turbulence on the diffusion and drift of cosmic-rays in the heliosphere is imperative for a better understanding of cosmic-ray modulation. This study presents an ab initio model for cosmic-ray modulation, incorporating for the first time the results yielded by a two-component turbulence transport model. The latter model is solved for solar minimum heliospheric conditions, utilizing boundary values chosen in such a way that the results of this model are in fair to good agreement with spacecraft observations of turbulence quantities, not only in the ecliptic plane, but also along the out-of-ecliptic trajectory of the Ulysses spacecraft. These results are employed as inputs for modelled slab and 2D turbulence energy spectra, which in turn are used as inputs for parallel mean free paths based on those derived from quasi-linear theory, and perpendicularmean free paths from extended nonlinear guiding center theory. The modelled 2D spectrum is chosen based on physical considerations, with a drop-off at the very lowest wavenumbers commencing at the 2D outerscale. There currently exist no models or observations for this quantity, and it is the only free parameter in this study. The use of such a spectrum yields a non-divergent 2D ultrascale, which is used as an input for the reduction terms proposed to model the effects of turbulence on cosmic-ray drifts. The resulting diffusion and drift coefficients are applied to the study of galactic cosmic-ray protons, electrons, antiprotons, and positrons using a three-dimensional, steady-state numerical cosmic-ray modulation code. The magnitude and spatial dependence of the 2D outerscale is demonstrated to have a significant effect on computed cosmic-ray intensities. A form for the 2D outerscale was found that resulted in computed cosmic-ray intensities, for all species considered, in reasonable agreement with multiple spacecraft observations. Computed galactic electron intensities are shown to be particularly sensitive to choices of parameters pertaining to the dissipation range of the slab turbulence spectrum, and certain models for the onset wavenumber of the dissipation range could be eliminated in this study.<br>Thesis (PhD (Physics))--North-West University, Potchefstroom Campus, 2013
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Lin, Charlette. "Out of Sight Out of Mind? The Effects of Prior Study and Visual Attention on Word Identification." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1430322757.

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Nikitin, Vyacheslav Y. "Parameter Dependencies in an Accumulation-to-Threshold Model of Simple Perceptual Decisions." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1442166546.

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Books on the topic "Drift-diffusion model"

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Schmithüsen, Bernhard. Grid adaption for the stationary two-dimensional drift diffusion model in semiconductor device simulation. Hartung-Gorre, 2002.

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Hänsch, W. The drift diffusion equation and its applications in MOSFET modeling. Springer-Verlag, 1991.

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Hänsch, W. The drift diffusion equation and its applications in MOSFET modeling. Springer-Verlag, 1991.

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Figdor, Carrie. Cases. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198809524.003.0003.

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Chapter 3 introduces the use of mathematical models and modeling practices in contemporary biological and cognitive sciences. The familiar Lotka–Volterra model of predator–prey relations is used to explain these practices and show how they promote the extensions of predicates, including psychological predicates, into new and often unexpected domains. It presents two models of cognitive capacities that were developed to explain human behavioral data: Ratcliff’s drift-diffusion model of decision-making and Sutton and Barto’s temporal difference model of reinforcement learning. These are now used for fruit flies and neural populations. It also discusses contemporary and ongoing attempts to revise psychological concepts in response to empirical discovery.
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Book chapters on the topic "Drift-diffusion model"

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Jüngel, Ansgar. "The Isentropic Drift-diffusion Model." In Quasi-hydrodynamic Semiconductor Equations. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8334-4_3.

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Bandyopadhyay, Supriyo. "Boltzmann Transport: Beyond the Drift–Diffusion Model." In Physics of Nanostructured Solid State Devices. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1141-3_2.

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Nathan, A., and T. Manku. "Piezoresistance and the Drift-Diffusion Model in Strained Silicon." In Simulation of Semiconductor Devices and Processes. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-6619-2_22.

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Reznik, D. "Generalised Drift-Diffusion Model of Bipolar Transport in Semiconductors." In Simulation of Semiconductor Devices and Processes. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-6619-2_61.

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Bandyopadhyay, Supriyo. "Charge and Current in Solids: The Classical Drift–Diffusion Model." In Physics of Nanostructured Solid State Devices. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1141-3_1.

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Shoji, Isao. "Estimation of Diffusion Parameters by a Nonparametric Drift Function Model." In Intelligent Data Engineering and Automated Learning. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45080-1_29.

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Hinze, Michael, and René Pinnau. "Optimal Control of the Drift Diffusion Model for Semiconductor Devices." In Optimal Control of Complex Structures. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8148-7_8.

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Hinze, Michael, Martin Kunkel, and Morten Vierling. "POD Model Order Reduction of Drift-Diffusion Equations in Electrical Networks." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0089-5_10.

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Cancès, Clément, Claire Chainais Hillairet, Jürgen Fuhrmann, and Benoît Gaudeul. "On Four Numerical Schemes for a Unipolar Degenerate Drift-Diffusion Model." In Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43651-3_13.

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Gajewski, H. "On uniqueness of solutions to the drift-diffusion-model of semiconductor devices." In Mathematical Modelling and Simulation of Electrical Circuits and Semiconductor Devices. Birkhäuser Basel, 1994. http://dx.doi.org/10.1007/978-3-0348-8528-7_14.

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Conference papers on the topic "Drift-diffusion model"

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Mesbah, S., K. Bendib-Kalache, A. Bendib, El-Hachemi Amara, Saïd Boudjemai, and Djamila Doumaz. "Generalized Drift-Diffusion Model In Semiconductors." In LASER AND PLASMA APPLICATIONS IN MATERIALS SCIENCE: First International Conference on Laser Plasma Applications in Materials Science—LAPAMS’08. AIP, 2008. http://dx.doi.org/10.1063/1.2999949.

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Abouchabaka, J., R. Aboulaich, A. Nachaoui, and A. Souissi. "The study of a drift-diffusion model." In ICM'2001 Proceedings. 13th International Conference on Microelectronics. IEEE, 2001. http://dx.doi.org/10.1109/icm.2001.997485.

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Reverdy, Paul, B. Deniz Ilhan, and Daniel E. Koditschek. "A drift-diffusion model for robotic obstacle avoidance." In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7354248.

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Pedersen, Mads, and Michael Frank. "Toolbox for the Reinforcement Learning Drift Diffusion Model." In 2019 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1380-0.

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Erlebach, A., K. H. Lee, and F. M. Bufler. "Empirical ballistic mobility model for drift-diffusion simulation." In ESSDERC 2016 - 46th European Solid-State Device Research Conference. IEEE, 2016. http://dx.doi.org/10.1109/essderc.2016.7599675.

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Liu, Tao, Yongqing Huang, Qingtao Chen, et al. "Transient simulation of UTC-PD using drift-diffusion model." In 2017 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD). IEEE, 2017. http://dx.doi.org/10.1109/nusod.2017.8010030.

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ROMANO, V., M. TORRISI, and R. TRACINÀ. "SYMMETRY ANALYSIS FOR THE QUANTUM DRIFT-DIFFUSION MODEL OF SEMICONDUCTORS." In Proceedings of the 13th Conference on WASCOM 2005. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773616_0062.

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Donnarumma, Gesualdo, Janusz Wozny, and Zbigniew Lisik. "Anisotropic drift diffusion model for 4H-, 6H-SiC devices simulation." In 2009 International Semiconductor Device Research Symposium (ISDRS 2009). IEEE, 2009. http://dx.doi.org/10.1109/isdrs.2009.5378258.

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Berezing, Y. A., and O. E. Dmitrieva. "The Splitting Scheme For The Drift-diffusion Model Of Semiconductors." In [1987] NASECODE V: Fifth International Conference on the Numerical Analysis of Semiconductor Devices and Integrated Circuits. IEEE, 1987. http://dx.doi.org/10.1109/nascod.1987.721173.

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Wei-Lun Wang, Heng-Sheng Huang, Yu-Hao Chao, et al. "I-V model of nano nMOSFETs incorporating drift and diffusion current." In 2017 6th International Symposium on Next-Generation Electronics (ISNE). IEEE, 2017. http://dx.doi.org/10.1109/isne.2017.7968715.

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Reports on the topic "Drift-diffusion model"

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Tan, Cheng-Yan. A simple drift-diffusion model for calculating the neutralization time of H- in xe gas for choppers placed in the LEBT. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/974354.

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