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Journal articles on the topic 'Drift-diffusion model'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Lillo, Fabrizio, and Rosario N. Mantegna. "Drift-controlled anomalous diffusion: A solvable Gaussian model." Physical Review E 61, no. 5 (2000): R4675—R4678. http://dx.doi.org/10.1103/physreve.61.r4675.

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12

Gallego, Samy, and Florian Méhats. "Entropic Discretization of a Quantum Drift-Diffusion Model." SIAM Journal on Numerical Analysis 43, no. 5 (2005): 1828–49. http://dx.doi.org/10.1137/040610556.

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13

Zhou, Likai. "Double-smoothed drift estimation of jump-diffusion model." Communications in Statistics - Theory and Methods 46, no. 8 (2016): 4137–49. http://dx.doi.org/10.1080/03610926.2015.1078479.

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14

Refaat, Tamer F. "Drift-diffusion model for reach-through avalanche photodiodes." Optical Engineering 40, no. 9 (2001): 1928. http://dx.doi.org/10.1117/1.1396655.

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15

Ben Abdallah, Naoufel, Florian Méhats, and Nicolas Vauchelet. "Analysis of a Drift-Diffusion-Schrödinger–Poisson model." Comptes Rendus Mathematique 335, no. 12 (2002): 1007–12. http://dx.doi.org/10.1016/s1631-073x(02)02612-2.

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16

Nguyen, Khanh P., Krešimir Josić, and Zachary P. Kilpatrick. "Optimizing sequential decisions in the drift–diffusion model." Journal of Mathematical Psychology 88 (February 2019): 32–47. http://dx.doi.org/10.1016/j.jmp.2018.11.001.

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17

Qiangchang, Ju, and Chen Li. "Semiclassical limit for bipolar quantum drift-diffusion model." Acta Mathematica Scientia 29, no. 2 (2009): 285–93. http://dx.doi.org/10.1016/s0252-9602(09)60029-1.

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18

Gallego, Samy, and Florian Méhats. "Numerical approximation of a quantum drift-diffusion model." Comptes Rendus Mathematique 339, no. 7 (2004): 519–24. http://dx.doi.org/10.1016/j.crma.2004.07.022.

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19

Pinnau, René. "A REVIEW ON THE QUANTUM DRIFT DIFFUSION MODEL." Transport Theory and Statistical Physics 31, no. 4-6 (2002): 367–95. http://dx.doi.org/10.1081/tt-120015506.

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20

Abouchabaka, Jaafar, Rajae Aboulaïch, Abdeljalil Nachaoui, and Ali Souissi. "A decoupled algorithm for a drift-diffusion model." Mathematical Methods in the Applied Sciences 28, no. 11 (2005): 1291–313. http://dx.doi.org/10.1002/mma.613.

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21

Xue, Jingming, Mary A. Peterson, and Robert C. Wilson. "A drift diffusion model of figure-ground perception." Journal of Vision 22, no. 14 (2022): 4205. http://dx.doi.org/10.1167/jov.22.14.4205.

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22

Lee, Kwang-Ho, Tag-Gyeom Kim, and Yong-Hwan Cho. "Influence of Tidal Current, Wind, and Wave in Hebei Spirit Oil Spill Modeling." Journal of Marine Science and Engineering 8, no. 2 (2020): 69. http://dx.doi.org/10.3390/jmse8020069.

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The purpose of this study is to investigate the effects of three external forces (tidal current, wind, and waves) on the movement of oil spilled during the Hebei Spirit oil spill accident. The diffusion of the spilled oil was simulated by using a random walk (RW) model that tracks the movement caused by advection-diffusion assuming oil as particles. For oil simulation, the wind drift current generated by wind and tidal current fields were computed by using the environmental fluid dynamics code (EFDC) model. Next, the wave fields were simulated by using the simulating waves nearshore (SWAN) model, and the Stokes drift current fields were calculated by applying the equation proposed by Stokes. The computed tidal currents, wind drift currents, and Stokes drift currents were applied as input data to the RW model. Then, oil diffusion distribution for each external force component was investigated and compared with that obtained from satellite images. When the wind drift currents and Stokes drift currents caused by waves were considered, the diffusion distribution of the spilled oil showed good agreement with that obtained from the observation.

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23

Jüngel, Ansgar, and Paola Pietra. "A Discretization Scheme for a Quasi-Hydrodynamic Semiconductor Model." Mathematical Models and Methods in Applied Sciences 07, no. 07 (1997): 935–55. http://dx.doi.org/10.1142/s0218202597000475.

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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.

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24

Pisarenko, Ivan, and Eugeny Ryndin. "Drift-Diffusion Simulation of High-Speed Optoelectronic Devices." Electronics 8, no. 1 (2019): 106. http://dx.doi.org/10.3390/electronics8010106.

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In this paper, we address the problem of research and development of the advanced optoelectronic devices designed for on-chip optical interconnections in integrated circuits. The development of the models, techniques, and applied software for the numerical simulation of carrier transport and accumulation in high-speed AIIIBV (A and B refer to group III and V semiconductors, respectively) optoelectronic devices is the purpose of the paper. We propose the model based on the standard drift-diffusion equations, rate equation for photons in an injection laser, and complex analytical models of carrier mobility, generation, and recombination. To solve the basic equations of the model, we developed the explicit and implicit techniques of drift-diffusion numerical simulation and applied software. These aids are suitable for the stationary and time-domain simulation of injection lasers and photodetectors with various electrophysical, constructive, and technological parameters at different control actions. We applied the model for the simulation of the lasers with functionally integrated amplitude and frequency modulators and uni-travelling-carrier photodetectors. According to the results of non-stationary simulation, it is reasonable to optimize the parameters of the lasers-modulators and develop new construction methods aimed at the improvement of photodetectors’ response time.

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25

Hübner, Ronald, and Thomas Pelzer. "Improving parameter recovery for conflict drift-diffusion models." Behavior Research Methods 52, no. 5 (2020): 1848–66. http://dx.doi.org/10.3758/s13428-020-01366-8.

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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.

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26

Jüngel, A. "Numerical Approximation of a Drift-Diffusion Model for Semiconductors with Nonlinear Diffusion." ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 75, no. 10 (1995): 783–99. http://dx.doi.org/10.1002/zamm.19950751016.

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27

Jiang, George J., and John L. Knight. "A Nonparametric Approach to the Estimation of Diffusion Processes, With an Application to a Short-Term Interest Rate Model." Econometric Theory 13, no. 5 (1997): 615–45. http://dx.doi.org/10.1017/s0266466600006101.

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In this paper, we propose a nonparametric identification and estimation procedure for an ltd diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general ltd diffusion processes and avoids any functional form specification for either the drift function or the diffusion function. It is shown that under certain regularity conditions the nonparametric diffusion function estimator is pointwise consistent and asymptotically follows a normal mixture distribution. Under stronger conditions, a consistent nonparametric estimator of the drift function is also derived based on the diffusion function estimator and the marginal density of the process. An application of the nonparametric technique to a short-term interest rate model involving Canadian daily 3-month treasury bill rates is also undertaken. The estimation results provide evidence for rejecting the common parametric or semiparametric specifications for both the drift and diffusion functions.

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28

NISHIBATA, SHINYA, NAOTAKA SHIGETA, and MASAHIRO SUZUKI. "ASYMPTOTIC BEHAVIORS AND CLASSICAL LIMITS OF SOLUTIONS TO A QUANTUM DRIFT-DIFFUSION MODEL FOR SEMICONDUCTORS." Mathematical Models and Methods in Applied Sciences 20, no. 06 (2010): 909–36. http://dx.doi.org/10.1142/s0218202510004477.

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This paper discusses a time global existence, asymptotic behavior and a singular limit of a solution to the initial boundary value problem for a quantum drift-diffusion model of semiconductors over a one-dimensional bounded domain. Firstly, we show a unique existence and an asymptotic stability of a stationary solution for the model. Secondly, it is shown that the time global solution for the quantum drift-diffusion model converges to that for a drift-diffusion model as the scaled Planck constant tends to zero. This singular limit is called a classical limit. Here these theorems allow the initial data to be arbitrarily large in the suitable Sobolev space. We prove them by applying an energy method.

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29

ARABSHAHI, H., REZAEE ROKN-ABADI, and S. GOLAFROZ. "COMPARISON OF TWO-VALLEY HYDRODYNAMIC MODEL IN BULK SiC AND ZnO MATERIALS." Modern Physics Letters B 23, no. 23 (2009): 2807–18. http://dx.doi.org/10.1142/s0217984909020916.

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This report reviews the feasibility of two-dimensional hydrodynamic models in bulk SiC and ZnO semiconductor materials. Although the single-gas hydrodynamic model is superior to the drift-diffusion or energy balance model, it is desirable to direct the efforts of future research in the direction of multi-valley hydrodynamic models. The hydrodynamic 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 the drift-diffusion model and the energy balance model are no longer applicable. Results of extensive numerical simulations are shown for SiC and ZnO materials, which are in fair agreement with other theoretical or experimental methods.

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30

Dong, Jian Wei. "On the Multidimensional Bipolar Isothermal Quantum Drift-Diffusion Model." Advanced Materials Research 466-467 (February 2012): 186–90. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.186.

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The bipolar isothermal quantum drift-diffusion model in two or three space dimensions with initial value and periodic boundary conditions is investigated. The global existence of weak solution to the problem is obtained by using semi-discretizing in time and entropy estimate. Furthermore, it is shown that the solution to the problem exponentially approaches its mean value as time increases to infinity by using a series of inequality technique.

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31

Yang, Yi, Robert A. Nawrocki, Richard M. Voyles, and Haiyan H. Zhang. "A Fractional Drift Diffusion Model for Organic Semiconductor Devices." Computers, Materials & Continua 69, no. 1 (2021): 237–66. http://dx.doi.org/10.32604/cmc.2021.017439.

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32

Nagatani, Takashi. "Growth model with phase transition: Drift-diffusion-limited aggregation." Physical Review A 39, no. 1 (1989): 438–41. http://dx.doi.org/10.1103/physreva.39.438.

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33

Pinnau, René. "Numerical approximation of the transient quantum drift diffusion model." Nonlinear Analysis: Theory, Methods & Applications 47, no. 9 (2001): 5849–60. http://dx.doi.org/10.1016/s0362-546x(01)00706-4.

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34

Chen, Xiuqing, Li Chen, and Huaiyu Jian. "The Dirichlet problem of the quantum drift-diffusion model." Nonlinear Analysis: Theory, Methods & Applications 69, no. 9 (2008): 3084–92. http://dx.doi.org/10.1016/j.na.2007.09.003.

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35

Pisarenko, I., E. Ryndin, and M. Denisenko. "Diffusion-drift model of injection lasers with double heterostructure." Journal of Physics: Conference Series 586 (January 30, 2015): 012015. http://dx.doi.org/10.1088/1742-6596/586/1/012015.

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36

Reznik, D., and W. Gerlach. "Generalised drift-diffusion model of bipolar transport in semiconductors." Electrical Engineering 79, no. 3 (1996): 219–25. http://dx.doi.org/10.1007/bf01232790.

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37

Friedman, Avner, and Wenxiong Liu. "An augmented drift-diffusion model in a semiconductor device." Journal of Mathematical Analysis and Applications 168, no. 2 (1992): 401–12. http://dx.doi.org/10.1016/0022-247x(92)90168-d.

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38

Chen, Xiuqing, and Li Chen. "Initial time layer problem for quantum drift-diffusion model." Journal of Mathematical Analysis and Applications 343, no. 1 (2008): 64–80. http://dx.doi.org/10.1016/j.jmaa.2008.01.015.

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39

Chen, Qiang, and Ping Guan. "Weak solutions to the stationary quantum drift-diffusion model." Journal of Mathematical Analysis and Applications 359, no. 2 (2009): 666–73. http://dx.doi.org/10.1016/j.jmaa.2009.06.030.

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40

Brauer, Elizabeth J., Marek Turowski, and James M. McDonough. "Additive Decomposition Applied to the Semiconductor Drift-Diffusion Model." VLSI Design 8, no. 1-4 (1998): 393–99. http://dx.doi.org/10.1155/1998/96170.

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A new numerical method for semiconductor device simulation is presented. The additive decomposition method has been successfully applied to Burgers' and Navier-Stokes equations governing turbulent fluid flow by decomposing the equations into large-scale and small-scale parts without averaging. The additive decomposition (AD) technique is well suited to problems with a large range of time and/or space scales, for example, thermal-electrical simulation of power semiconductor devices with large physical size. Furthermore, AD adds a level of parallelization for improved computational efficiency. The new numerical technique has been tested on the 1-D drift-diffusion model of a p-i-n diode for reverse and forward biases. Distributions of φ, n and p have been calculated using the AD method on a coarse large-scale grid and then in parallel small-scale grid sections. The AD results agreed well with the results obtained with a traditional one-grid approach, while potentially reducing memory requirements with the new method.

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41

Yamada, Y. "Energy transport drift-diffusion model for submicrometer GaAs MESFETs." Microelectronics Journal 28, no. 5 (1997): 561–69. http://dx.doi.org/10.1016/s0026-2692(96)00101-2.

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42

Xu, Xiangsheng. "A drift-diffusion model for semiconductors with temperature effects." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 139, no. 5 (2009): 1101–19. http://dx.doi.org/10.1017/s0308210507001187.

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43

Luzardo, Andre, Elliot A. Ludvig, and François Rivest. "An adaptive drift-diffusion model of interval timing dynamics." Behavioural Processes 95 (May 2013): 90–99. http://dx.doi.org/10.1016/j.beproc.2013.02.003.

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44

Baro, M., N. Ben Abdallah, P. Degond, and A. El Ayyadi. "A 1D coupled Schrödinger drift-diffusion model including collisions." Journal of Computational Physics 203, no. 1 (2005): 129–53. http://dx.doi.org/10.1016/j.jcp.2004.08.009.

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45

Unterreiter, A., and S. Volkwein. "Optimal Control of the Stationary Quantum Drift-Diffusion Model." Communications in Mathematical Sciences 5, no. 1 (2007): 85–111. http://dx.doi.org/10.4310/cms.2007.v5.n1.a4.

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46

Karamarković, J. P., and N. D. Janković. "Modification of drift-diffusion model for short base transport." Electronics Letters 36, no. 24 (2000): 2047. http://dx.doi.org/10.1049/el:20001411.

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47

Fisher, Geoffrey. "An attentional drift diffusion model over binary-attribute choice." Cognition 168 (November 2017): 34–45. http://dx.doi.org/10.1016/j.cognition.2017.06.007.

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48

Ju, Qiang Chang. "The semiclassical limit in the quantum drift-diffusion model." Acta Mathematica Sinica, English Series 25, no. 2 (2009): 253–64. http://dx.doi.org/10.1007/s10114-008-7098-z.

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49

Fang, W. F., and K. Itoi. "On the Time-Dependent Drift-Diffusion Model for Semiconductors." Journal of Differential Equations 117, no. 2 (1995): 245–80. http://dx.doi.org/10.1006/jdeq.1995.1054.

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50

Chau, Edwin, Carolyn A. Murray, and Ladan Shams. "Hierarchical drift diffusion modeling uncovers multisensory benefit in numerosity discrimination tasks." PeerJ 9 (October 27, 2021): e12273. http://dx.doi.org/10.7717/peerj.12273.

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Studies of accuracy and reaction time in decision making often observe a speed-accuracy tradeoff, where either accuracy or reaction time is sacrificed for the other. While this effect may mask certain multisensory benefits in performance when accuracy and reaction time are separately measured, drift diffusion models (DDMs) are able to consider both simultaneously. However, drift diffusion models are often limited by large sample size requirements for reliable parameter estimation. One solution to this restriction is the use of hierarchical Bayesian estimation for DDM parameters. Here, we utilize hierarchical drift diffusion models (HDDMs) to reveal a multisensory advantage in auditory-visual numerosity discrimination tasks. By fitting this model with a modestly sized dataset, we also demonstrate that large sample sizes are not necessary for reliable parameter estimation.

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