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

Autran, Jean‐Luc; Glorieux, Maximilien; Munteanu, Daniéla; Clerc, Sylvain; Gasiot, Gilles; Roché, Philippe

doi:10.1016/j.microrel.2014.07.088

Cited by 8 publications

(12 citation statements)

References 15 publications

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“…This voltage is susceptible to vary during the transient event due to circuit retroaction. (Reprinted with permission from Autran et al [10], this case is called "with SCR feedback." By opposition, we name "without SCR feedback" the case when the same (fixed) SCR parameters are maintained during the entire transient simulation.…”

Section: Transient Simulation Of a Cmos Invertermentioning

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 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

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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.

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Section: Transient Simulation Of a Cmos Invertermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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“…Typical orders of magnitude for such numerical simulation are the following: 10 5 charge packets, 250 time values ranging from 10 -15 to 10 -8 s with a geometrical progression, total simulation time of about 1 minute on quad-core workstation. Other details about equations and numerical implementations of both RWDD model and circuit solving can be found in[9][10][11].…”

mentioning

confidence: 99%

Modelling and simulation of SEU in bulk Si and Ge SRAM

Moindjie

Munteanu

Autran

2019

Microelectronics Reliability

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In this work, the random-walk drift-diffusion (RWDD) model has been coupled to a circuit simulator to investigate single event upsets (SEU) induced by alpha particles in SRAM cells with silicon or germanium as bulk material. The impact of semiconductor charge generation and transport properties on the SEU mechanisms is quantified and discussed in a first-order approach considering ideal materials and generic devices. Our results suggest that the radiation response of Ge-based SRAM should be similar to the one observed for Si-SRAM, the benefits of higher mobilities at circuit level for germanium offsetting the negative impact of a relatively low energy value for electron-pair creation on transient current pulse magnitudes.

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“…The charge diffusion and collection mechanisms need to be modeled using analytical [68], [61], [77] or simplified physic equations [69], [80]. The transport mechanisms of the induced charges can be based on ambipolar diffusion model [91] taking into account recombination processes [68], [79] and carrier-carrier scattering phenomenon [61].…”

Section: A Toward Integrated "Framework" For Set Modeling In Electromentioning

confidence: 99%

“…The collection mechanisms of induced charges must be based on dynamic collection model [68] which leads to take into account the modulation of the electrical field at the junction between the diffusion regions (drain and source) and the substrate. These models are layout and technology dependent, as presented in [57], [68], [69], [79], [80]. The inputs of these models can be provided by foundries, designers or from the ITRS roadmaps.…”

Section: A Toward Integrated "Framework" For Set Modeling In Electromentioning

confidence: 99%

Modeling Single Event Transients in Advanced Devices and ICs

Artola

Gaillardin

Hubert

et al. 2015

IEEE Trans. Nucl. Sci.

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The ability for Single Event Transients (SETs) to induce soft errors in Integrated Circuits (ICs) was predicted for the first time by Wallmark and Marcus in the early 60's [1] and was confirmed to be a serious issue thirty years later. In the 90's microelectronic technologies reached the "deep submicron" era, allowing high density ICs working at frequencies faster than hundreds of MHz. This new paradigm changed the status of SETs to become a major source of reliability losses. Huge efforts have thus been made to characterize SETs in microelectronics, either using experiments or by simulation, in order to reveal key factors leading to SET occurrence, propagation and capture in modern ICs. In this context, modeling and simulation are of primary importance to get accurate SET predictions. This paper focuses on modeling SETs in innovative electronic devices which involves modeling steps at different scales, from ionizing particle to circuit response. After a brief review of the state-of-the art of modeling at each scale, this paper will discuss current capabilities and intrinsic limitations of SET modeling, the incoming challenges in advanced devices and ICs, and finally the methodologies to improve SET simulation and prediction for future technologies.

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

Cited by 8 publications

References 15 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

Modelling and simulation of SEU in bulk Si and Ge SRAM

Modeling Single Event Transients in Advanced Devices and ICs

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