Drift Diffusion Research Articles

Analysis of quantum confinement in nanosheet FETs by using a quantum drift diffusion model

AbstractIn this paper, we have analyzed quantum confinement effects in nanosheet MOSFETs by using a quantum drift‐diffusion (QDD) model. The QDD model is a device simulator which allows to simulate quantum confinement effects in the inversion layer for advanced MOSFETs. The quantum confinement effects in nanosheets have been analyzed by comparing the simulation results by QDD and drift‐diffusion (DD) model. The drain current ratio of DD to QDD is 250.1% at VG = 0.5 V and 180.1% at VG = 0 V. While the maximum electron density of DD exists at the interface between the insulator and the silicon sheet, that of QDD goes to near the center of the silicon sheet. The electron areal density ratio of DD to QDD is 118.3% in the direction of the 10 nm width of the silicon sheet and 176.9% in the direction of the 4 nm width.

Pallidal neuromodulation of the explore/exploit trade-off in decision-making.

Every decision that we make involves a conflict between exploiting our current knowledge of an action's value or exploring alternative courses of action that might lead to a better, or worse outcome. The sub-cortical nuclei that make up the basal ganglia have been proposed as a neural circuit that may contribute to resolving this explore-exploit 'dilemma'. To test this hypothesis, we examined the effects of neuromodulating the basal ganglia's output nucleus, the globus pallidus interna, in patients who had undergone deep brain stimulation (DBS) for isolated dystonia. Neuromodulation enhanced the number of exploratory choices to the lower value option in a two-armed bandit probabilistic reversal-learning task. Enhanced exploration was explained by a reduction in the rate of evidence accumulation (drift rate) in a reinforcement learning drift diffusion model. We estimated the functional connectivity profile between the stimulating DBS electrode and the rest of the brain using a normative functional connectome derived from heathy controls. Variation in the extent of neuromodulation induced exploration between patients was associated with functional connectivity from the stimulation electrode site to a distributed brain functional network. We conclude that the basal ganglia's output nucleus, the globus pallidus interna, can adaptively modify decision choice when faced with the dilemma to explore or exploit.

Quasi-Fermi-Based Charge Transport Scheme for Device Simulation in Cryogenic, Wide Bandgap, and High-Voltage Applications

We present a novel approach to solving the transport problem in semiconductors. We reformulate the drift-diffusion (DD) equations in terms of the quasi-Fermi-energies as solution variables; a drastic increase in numerical stability is achieved, which permits the simulation of devices at cryogenic temperatures as well as wide bandgap devices using double precision arithmetic, instead of extended precision arithmetic which would otherwise be required to solve these applications using regular DD.

Assessing subjective prime awareness on a trial-by-trial basis interferes with masked semantic priming effects.

Ratings of perceptual experience on a trial-by-trial basis are increasingly used in masked priming studies to assess prime awareness. It is argued that such subjective ratings more adequately capture the content of phenomenal consciousness compared to the standard objective psychophysical measures obtained in a session after the priming experiment. However, the concurrent implementation of the ratings within the priming experiment might alter magnitude and processes underlying semantic priming, because participants try to identify the masked prime. In the present study, we therefore compared masked semantic priming effects assessed within the classical sequential procedure, in which prime identification is psychophysically assessed after the priming experiment, with those obtained in a condition, in which prime awareness is rated within the priming experiment. Two groups of participants performed a lexical decision task (LDT) on targets preceded by masked primes of 20, 40, or 60 ms durations, to induce the variability of prime awareness. One group additionally rated prime visibility trials-wise using the Perceptual Awareness Scale (PAS), whereas the other group only performed the LDT. Analysis of reaction times (RTs) as well as drift diffusion modeling revealed general priming effects on RT and drift rate only in the PAS-absent group. In the PAS-present group, residual priming effects on RT and the non-decisional component t0 were obtained for trials with rated prime awareness. This shows that assessing subjective perceptual experience on a trial-by-trial basis heavily interferes with semantic processes underlying masked priming, presumably due to attentional demands associated with concurrent prime identification. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Multivariate decoding methods reveal how speaking two dialects affects executive functions.

How speaking two languages affects executive functions has been a long-standing debate and the mechanisms underlying the observed cognitive advantages of bilingualism remain unspecified. Here, using multivariate pattern classification methods, we decoded spatial patterns of neural signals associated with Flanker task performance in mono-dialectal and bi-dialectal speakers of Chinese. While univariate approach to even-related potentials (ERPs) showed no between-group difference, decoding accuracy of ERPs was reduced in bi-dialectal as compared to mono-dialectal speakers in both congruent-neutral and incongruent-neutral classifications. There was no effect of bidialectalism, however, on decoding accuracy of alpha-band oscillations, an electrophysiological index implicated in inhibition. Behavioural data analysed using the Drift Diffusion Model (DDM) showed facilitating effects of bidialectalism on non-decision times but no effect on drift rates. These findings demonstrate that using two dialects on a daily basis enhances general attentional deployment rather than affecting specific component of executive functions such as inhibitory control. Given that the two dialects of Chinese differed almost exclusively in phonology, the bidialectalism effect was most likely motivated by resolving phonological competition at lexical processing level.

Darwinian evolution as Brownian motion on the simplex: A geometric perspective on stochastic replicator dynamics

We prove that stochastic replicator dynamics can be interpreted as intrinsic Brownian motion on the simplex equipped with the Aitchison geometry. As an immediate consequence, we derive three approximation results in the spirit of Wong–Zakai approximation, Donsker’s invariance principle and a JKO-scheme. Using the Fokker–Planck equation and Wasserstein-contraction estimates, we also study the long time behavior of the stochastic replicator equation, as an example of a nongradient drift diffusion on the Aitchison simplex.

"Reliable organisms from unreliable components" revisited: the linear drift, linear infinitesimal variance model of decision making.

Diffusion models of decision making, in which successive samples of noisy evidence are accumulated to decision criteria, provide a theoretical solution to von Neumann's (1956) problem of how to increase the reliability of neural computation in the presence of noise. I introduce and evaluate a new neurally-inspired dual diffusion model, the linear drift, linear infinitesimal variance (LDLIV) model, which embodies three features often thought to characterize neural mechanisms of decision making. The accumulating evidence is intrinsically positively-valued, saturates at high intensities, and is accumulated for each alternative separately. I present explicit integral-equation predictions for the response time distribution and choice probabilities for the LDLIV model and compare its performance on two benchmark sets of data to three other models: the standard diffusion model and two dual diffusion model composed of racing Wiener processes, one between absorbing and reflecting boundaries and one with absorbing boundaries only. The LDLIV model and the standard diffusion model performed similarly to one another, although the standard diffusion model is more parsimonious, and both performed appreciably better than the other two dual diffusion models. I argue that accumulation of noisy evidence by a diffusion process and drift rate variability are both expressions of how the cognitive system solves von Neumann's problem, by aggregating noisy representations over time and over elements of a neural population. I also argue that models that do not solve von Neumann's problem do not address the main theoretical question that historically motivated research in this area.

Electrophysiological correlates of self-prioritization

Personally relevant stimuli exert a powerful influence on social cognition. What is not yet fully understood, however, is how early in the processing stream self-relevance influences decisional operations. Here we used a shape-label matching task in conjunction with electroencephalography and computational modeling to explore this issue. A theoretically important pattern of results was observed. First, a standard self-prioritization effect emerged indicating that responses to self-related items were faster and more accurate than responses to other-related stimuli. Second, a hierarchical drift diffusion model analysis revealed that this effect was underpinned by the enhanced uptake of evidence from self-related stimuli. Third, self-other discrimination during matching trials was observed at both early posterior N1 and late centro-parietal P3 components. Fourth, whereas the N1 was associated with the rate of information accumulation during decisional processing, P3 activity was linked with the evidential requirements of response selection. These findings elucidate the electrophysiological correlates of self-prioritization.

Modeling brain dynamics and gaze behavior: Starting point bias and drift rate relate to frontal midline theta oscillations

Frontal midline theta oscillatory dynamics have been implicated as an important neural signature of inhibitory control. However, most proactive cognitive control studies rely on behavioral tasks where individual differences are inferred through button presses. We applied computational modeling to further refine our understanding of theta dynamics in a cued anti-saccade task with gaze-contingent eye tracking. Using a drift diffusion model, increased frontal midline theta power during high-conflict, relative to low-conflict, trials predicted a more conservative style of responding through the starting point (bias). During both high- and low-conflict trials, increases in frontal midline theta also predicted improvements in response efficiency (drift rate). Regression analyses provided support for the importance of the starting point bias, which was associated with frontal midline theta over the course of the task above-and-beyond both drift rate and mean reaction time. Our findings provide a more thorough understanding of proactive gaze control by linking trial-by-trial increases of frontal midline theta to a shift in starting point bias facilitating a more neutral style of responding.

The Influence of Different Recombination Pathways on Hysteresis in Perovskite Solar Cells with Ion Migration

The impact of hysteresis on the power conversion efficiency (PCE) of perovskite solar cells (PSCs) still faces uncertainties despite the rapid development of perovskite photovoltaics. Although ion migration in perovskites is regarded as the chief culprit for hysteresis, charge carrier recombination pathways in PSCs are proposed to be necessary for the occurrence of hysteresis. Here, the impact of both bulk recombination and interface recombination on hysteresis in PSCs is investigated via drift–diffusion modeling. The simulation results demonstrate a direct correlation between recombination pathways and hysteresis in PSCs with ion migration. The simulation reveals that recombination pathways in PSCs will react to the variation in charge carrier distribution under different voltage scanning directions induced by ion migration in absorber layers, which leads to hysteresis in PSCs. Moreover, the hysteresis in normal (N-I-P) PSCs with different electron transport layers (ETLs) including sintered SnO2, SnO2 nano crystals and TiO2 is experimentally explored. The results demonstrate that multiple recombination pathways coupled with ion migration can lead to obvious hysteresis in fabricated PSCs which is consistent with simulation results. This work provides great insight into hysteresis management upon composition, additive and interface engineering in PSCs.

Weak mean attractors of stochastic p-Laplacian delay lattice systems driven by nonlinear noise

This paper deals with the existence and uniqueness of weak pullback mean random attractors for a class non-autonomous stochastic p-Laplacian delay lattice systems with local Lipschitz nonlinear terms driven by nonlinear noise defined on the entire integer set Z. We first establish the global well-posedness to non-autonomous stochastic p-Laplacian delay lattice system in C([τ,∞),L2(Ω,ℓ2)) when the nonlinear diffusion terms and drift terms are locally Lipschitz continuous functions, based on the uniform estimates of approximate solutions in a finite time-interval as well as an appropriate stopping time. We then define a mean random dynamical system via the solution operators and prove the existence and uniqueness of weak pullback mean random attractors in product Hilbert space L2(Ω,F;ℓ2)×L2(Ω,F;L2((−ρ,0),ℓ2)) under the condition that the constant λ in the system is relatively large.

Interface Scattering as a Nonlocal Transport Phenomenon in Semiconductors

Macroscopically non-local effects are common in electron transport in semiconductor devices, occurring whenever the mean free path and/or the deBroglie wavelength are not small compared to geometry/flow length scales. When such effects are important, standard diffusion-drift (DD) theory becomes inaccurate and in need of revision, with the best known example being density-gradient (DG) theory wherein a gradient term is added to the electron gas equation of state to approximate the effect of quantum non-locality. Here we consider a similarly motivated gradient correction to the electron-lattice interaction that accounts for non-locality in the transport physics. Versions of DD and DG theory with this correction are discussed, and then are applied to the analysis of long-channel field-effect transistors where they are found to provide a physics-based approach for modeling and understanding interface scattering.

What a Special Operations Cognitive Assessment Should Look Like.

Special Operations organizations have recently demonstrated their commitment to enhanced cognitive functioning and improving brain health through the development of a Cognitive Domain. However, as this new enterprise becomes supported by more resources and personnel, a critical question involves what cognitive assessments should be conducted to evaluate cognitive functions. The assessment itself forms a crux in the Cognitive Domain that could mislead cognitive practitioners if not properly applied. Here, the discussion addresses the most important criteria to satisfy in the development of a Special Operations cognitive assessment, including operational relevance, optimization, and speed. Cognitive assessments in this domain must incorporate the following: (1) a task with clear operational relevance to ensure meaningful results, (2) no ceiling effects so that performance can support cognitive enhancement initiatives, and (3) the task itself should impose a minimal time requirement to avoid creating a substantial logistical burden. A dynamic threat assessment task supported by drift diffusion modeling can meet all requisite criteria, while also providing more insight into decision parameters of Special Operations personnel than any currently used test. The discussion concludes with a detailed description of this recommended cognitive assessment task, as well as the research and development steps needed to support its application.

A novel TMOSFET ternary inverter based on hybrid conduction mechanism

With the development of complementary metal-oxide semiconductor (CMOS) technology, the feature size of mental-oxide-semiconductor field-effect-transistor (MOSFET) is continuously shrunk, the short channel effect becomes more and more serious, which makes the static power consumption increase, and now the static power consumption becomes a main source of the power consumption of the integrated circuits. Currently, the performance of CMOS binary logic processor is approaching a bottleneck; therefore the ternary logic becomes a research hotspot to promote the development of high-performance low-power integrated circuits. Compared with binary logic, ternary logic possesses a strong data expression capability, which can not only improve the data density, but also reduce the circuit power consumption and the system complexity. However, using binary devices to build ternary logic circuits requires a large number of components, and even the passive components, which makes it impossible to leverage the advantages of ternary logic. The other method of implementing ternary logic is to utilize innovative two-dimensional materials. This method requires a small number of components and obviates the need for passive components, but it faces the problem that the fabrication process is not mature and cannot be mass-produced. To solve these problems, in this paper by combining the tunneling and the drift diffusion mechanism, we propose a tunneling metal-oxide-semiconductor field-effect transistor (TMOSFET) with three-state characteristics that make it highly suitable for ternary logic design. Compared with other ternary logic schemes, the ternary inverter based on TMOSFET has the same circuit structure as binary inverter, which can simplify the circuit design. In this paper, the operational mechanism of this ternary inverter is studied, and the condition of three-state output of inverter is analyzed. It is found that when the operating voltage <i>V</i><sub>DD</sub> and the device turning voltage <i>V</i><sub>turn</sub> satisfy <i>V</i><sub>DD</sub>/<i>V</i><sub>turn</sub> ≈ 1.4, the input voltage ranges of the three output states are equivalent. In addition, the influence of TMOSFET transfer characteristic on this ternary inverter is also analyzed. This has certain reference significance for designing and studying ternary logic circuits in future.

All region analytical modeling of 2-D transition metal dichalcogenide FET by considering effect of fringing field and region-wise mobility

In this paper, for the first time an analytical all region model of Transition metal dichalcogenide based FET has been developed to represent the current–voltage characteristics of the device in ultra-short channel regime. Starting from Gauss’s law, Poisson’s equation is formulated and short channel subthreshold current model is developed subsequently. In order to include fringing field effect, the gaussian box is considered for top and bottom oxides along with channel. Next, Velocity saturation model is developed by considering mobile charges due to top-bottom gates and region-wise field dependent mobility in the channel utilizing the drift diffusion approach. Both the models are then unified by finding the natural points of transition from one model to other. The intraband source to drain tunneling has also been incorporated to develop generalized 2-D material based FET model. Veracity of the model has been verified with NEGF simulations for different device parameters and excellent congruence is observed. The effect of the interface trap charge density has also been included in the model and its effects on the transfer characteristics are investigated. • Analytical modeling of I-V characteristics for TMD materials applicable for all channel lengths. • Validation of model with atomistic simulator data for Sub 10 nm channel length. • Effects of double gate and fringing field have been included. • Source to drain tunneling has been included to increase model applicability for any 2D materials.

Abnormal evidence accumulation underlies the positive memory deficit in depression.

Healthy adults show better memory for low-arousal positive versus negative stimuli, but depression compromises this positive memory advantage. Existing studies are limited by small samples or analyses that provide limited insight into underlying mechanisms. Our study addresses these concerns by using a multistaged analysis, including diffusion modeling, to identify precise psychological processes underlying the positive memory advantage and its disruption by depression in a large sample. A total of 1,358 participants completed the BDI-II (Beck et al., 1996) and an emotional memory task. At encoding, participants judged whether positive and negative words were positive or self-descriptive. After a free recall test, participants viewed an equal mix of studied and unstudied words and judged whether each was "old" or "new"; if judged "old," they indicated whether the study source was a valence or self-reference judgment. We replicate the positive memory advantage and its decrease in depression in recall, recognition, and source accuracy. The hierarchical drift diffusion model (HDDM; Wiecki et al., 2013) revealed that higher BDI-II scores are associated with more efficient evidence accumulation for negative words in the recognition and source memory tasks. By contrast, evidence accumulation for positive words is unaffected by BDI-II during the recognition task but becomes less efficient with increased BDI-II during the source memory task. In conclusion, in a well-controlled design with a large sample, we find that depression reduces the positive memory advantage. HDDM analyses suggest that this reflects differential effects of depression on the speed of evidence accumulation during the retrieval of positive versus negative memories. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The modelling and simulation of perovskite solar cell consisting textile-based electrodes

Perovskite solar cell (PSC) consisting textile-based electrodes has been created novel features for future energy supply. Recently, Cs0.05(FA0.85MA0.15)0.95Pb(I0.85Br0.15)3 perovskite has appeared as a favourable candidate for flexible perovskite solar cells. However, a confined performance was acquired when the conventional Spiro-OMeTAD were used as hole transport layer (HTL) material because of suboptimal valance band offset (ΔEV) between Perovskite/HTL layers. In this study, a theoretical drift–diffusion​ modelling is used by finite volume method (FVM) to investigate performance of textile perovskite solar cell with double hole transport layers (HTLs) with graded band gap energies. Copper thiocyanate (CuSCN) and Nickel Oxide (NiO) are replaced as double HTLs, which dramatically enhanced the performance due to optimal upward step by step valance band offsets (ΔEV) between Perovskite/Textile Composite Electrode layers. It is shown that the decreasing of the metal–semiconductor schottky barrier height of electrodes leads to significant increase in fill factor (FF). Also, it is revealed that the enriching of perovskite carrier diffusion length by equipoising of carrier capture cross section, carrier total defect density and carrier thermal velocity substantially promote the overall device efficiency. The short-circuit current (Jsc) of 20.65 mA/cm 2, open-circuit voltage (Voc) of 1.19 V, fill factor (FF) of 73.57%, and power conversion efficiency (PCE) of 18.20% have been obtained by optimization of electrodes properties and perovskite layers. Remarkably, the advantageous of low cost, flexible, and chemical stable conducive textile composite, extends new opportunities in the commercial expansion of highly efficient photovoltaic textile in optoelectronic industry field.

Different fluid strategies for the simulation of a Helicon Plasma Thruster

AbstractDifferent fluid numerical strategies have been considered for the analysis of the plasma transport in a Helicon Plasma Thruster, namely the Drift Diffusion solution, the explicitly segregated solution, and the adoption of the Simple algorithm [Patankar & Spalding] for the coupled solution of the continuity and momentum balances. These strategies have been compared in terms of plasma profiles such as electron density and temperature, and their computational efficiency has been assessed. Finally, a sensitivity analysis over the neutrals' pressure has been performed in order to assess the validity of each proposed strategy for different collisional regimes.

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences.

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers' ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data - without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.

THE INFLUENCE OF THERMAL MOTION OF PARTICLES ON THE FORMATION OF CAVITIES IN PLASMA

The effect of the thermal motion of ions and electrons on the formation of plasma density cavities which appear due to inhomogeneous stochastic electric fields is considered. Using the equation of motion of plasma particles in a constant magnetic field and in an inhomogeneous stochastic electric field with a frequency of the order of lower hybrid oscillations, taking into account the thermal motion of particles, the diffusion coefficients and drift velocities of ions and electrons are obtained. These values are used in the Fokker-Planck equation to determine the stationary distribution of the plasma density due to the effect of an inhomogeneous stochastic electric field. The conditions for the thermal velocities of particles under which the formation of a cavity is possible are obtained.