Monte-Carlo Simulation of Particle Diffusion in Various Geometries and Application to Chemistry and Biology

Plante, Ianik; Cucinotta, Francis A.

doi:10.5772/53203

Cited by 14 publications

(21 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SRIM tables are used to compute both LET and range using appropriate numerical functions [14]. The accuracy of this charge discretization is ensured by the degree of "granularity" [11] that can be fine-tuned by selecting the packets size, in practice from 1 to 100 elementary charges. …”

Section: Charge Depositionmentioning

confidence: 99%

“…Contrary to this procedure, in the RWDD model, no meshing is need since charge packets have continuous coordinates. Also in RWDD, a random-walk algorithm [11] is used to model the diffusion process, and the drift-induced current is directly calculated using the electrical field present in the considered region. For a charge packet situated at the position (x, y, z) at time t, its new position at time t+δt is given by (x+δx, y+δy and z+δz), where dx, dy and dz are calculated as follows: …”

Section: Charge Transport Modelingmentioning

confidence: 99%

“…This approach is called random-walk drift-diffusion (RWDD) [9,10] and consists in a fast Monte Carlo particle method based on a random-walk process [11] that includes both the diffusion and the drift of carriers in an electric field that is nonconstant in both space and time. This method has been successfully used in previous works, as shown in Refs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Charge Collection Physical Modeling for Soft Error Rate Computational Simulation in Digital Circuits

Autran¹,

Munteanu²,

Moindjie³

et al. 2016

Modeling and Simulation in Engineering Sciences

View full text Add to dashboard Cite

This chapter describes a new computational approach for accurately modeling radiation-induced single-event transient current and charge collection at circuit level. This approach, called random-walk drift-diffusion (RWDD), is a fast Monte Carlo particle method based on a random-walk process that takes into account both diffusion and drift of carriers in a non-constant electric field both in space and time. After introducing the physical insights of the RWDD model, the chapter details the practical implementation of the method using an object-oriented programming language and its parallelization on graphical processing units. Besides, the capability of the approach to treat multiple node charge collection is presented. The chapter also details the coupling of the model either with an internal routine or with SPICE for circuit solving. Finally, the proposed approach is illustrated at device and circuit level, considering four different test vehicles in 65 nm technologies: a stand-alone transistor, a CMOS inverter, a SRAM cell and a flip-flop circuit. RWDD results are compared with data obtained from a full three-dimensional (3D) numerical approach (TCAD simulations) at transistor level. The importance of the circuit feedback on the charge-collection process is also demonstrated for devices connected to other circuit nodes.

show abstract

Section: Charge Depositionmentioning

confidence: 99%

Section: Charge Transport Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Charge Collection Physical Modeling for Soft Error Rate Computational Simulation in Digital Circuits

Autran¹,

Munteanu²,

Moindjie³

et al. 2016

Modeling and Simulation in Engineering Sciences

View full text Add to dashboard Cite

show abstract

“…In the present RWDD model, charge packets (i.e. particles) have continuous coordinates and no mesh grid is needed to solve the transport problem: the diffusive process is modeled using a random-walk algorithm [8] whereas the driftinduced motion is directly evaluated from the electrical field developed in the region of interest. Considering a charge packet initially at the position (x, y, z) at time t, its new position at time t + Dt is given by (x + Dy, y + Dy, z + Dz) with:…”

Section: Random-walk Drift-diffusion (Rwdd) Modelmentioning

confidence: 99%

“…Our methodology is a fast Monte Carlo particle method based on a random-walk process [8] that takes into account both diffusion and drift of carriers in a non-constant electric field both in space and time. This approach is called Random-Walk Drift-Diffusion (RWDD).…”

Section: Introductionmentioning

confidence: 99%

Particle Monte Carlo modeling of single-event transient current and charge collection in integrated circuits

Autran

Glorieux

Munteanu

et al. 2014

Microelectronics Reliability

View full text Add to dashboard Cite

Surface Plasmon Resonance Allows to Correlate Molecular Properties With Diffusion Coefficients of Linear Chain Alcohols

Basile,

Calcagno,

Pandino

et al. 2024

Chemistry A European J

View full text Add to dashboard Cite

Every biological and physicochemical process occurring in a fluid phase depends on the diffusion coefficient (D) of the species in solution. In the present work, a model to describe and fit the behaviour of D as a function of structure and extensive thermodynamics parameters in binary solutions of linear chain organic molecules is developed. Supporting experimental and computational evidences for this model are obtained by measuring D for a series of n‐alcohols through a novel surface plasmon resonance method and molecular dynamics simulations. This allows to propose a kind of combined analysis to explain the dependence of D on various thermodynamic and structural parameters. The results suggest that for small linear systems in the range from 0 to 200 g mol‐1 and under the assumption that the diffusive activation energy is a linear function of mass, D is strictly dependent on the molecular shape and on the relative strength of the solute‐solvent intermolecular forces represented by a parameter named R. The newly proposed approach can be utilized to characterize and monitor progressive changes in physicochemical properties for any investigated species upon increasing the dimension of the aggregate/molecule along a certain direction.

show abstract

Monte-Carlo Simulation of Particle Diffusion in Various Geometries and Application to Chemistry and Biology

Cited by 14 publications

References 32 publications

Charge Collection Physical Modeling for Soft Error Rate Computational Simulation in Digital Circuits

Charge Collection Physical Modeling for Soft Error Rate Computational Simulation in Digital Circuits

Particle Monte Carlo modeling of single-event transient current and charge collection in integrated circuits

Surface Plasmon Resonance Allows to Correlate Molecular Properties With Diffusion Coefficients of Linear Chain Alcohols

Contact Info

Product

Resources

About