Innovative Simulations of Heavy Ion Cross Sections in 130 nm CMOS SRAM

Correas, V.; Saigné, Frédéric; Sagnes, B.; Boch, J.; Gasiot, Gilles; Giot, D.; Roché, Philippe

doi:10.1109/tns.2007.910038

Cited by 34 publications

(19 citation statements)

References 14 publications

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“…In order to overstep these limitations, several approximated solutions have been developed in the literature: these approaches generally calculate the current transients induced by single events (SETs) in devices and circuits by decoupling the radiation-induced charge transport from the electrostatic problem. The so-called diffusion-collection model is one of the most interesting and physically accurate approaches developed for this purpose [4][5][6]. This model makes the assumption that the main carrier driving mechanisms is a pure spherical ambipolar diffusion in the semiconductor region.…”

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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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: 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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“…The I inj -V drain criterion could be useful in the case of SER simulation based on simplified current models [3,8,12], where the determination of a SEU criterion is often a concern. Then, a solution to criteria problems is to add a dynamic voltage at the collection electrode.…”

Section: Resultsmentioning

confidence: 99%

“…Once again, the "plateau" appears as an indicator of the memory cell bit flip. (1), 36% for (2) and 41% (3). The modeling of the RC Circuit may require more adjustment of the capacitance and resistance equivalent values… Indeed, the RC circuit is calibrated based on case (1), which explains better matching results with case (1).…”

Section: Analysis Of the "Plateau" Effectmentioning

confidence: 99%

“…Because the flipping time necessary to calculate the critical charge is not easy to determine, other key parameters, using the current peak value and its time characteristics were explored as the I max -t max or I max -Δt criteria [3,8,12]. However, these criteria would probably not be sufficient for future technologies because they usually only consider simple current injection at one electrode of the device [19].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of SEU Simulations for SRAM Cells Reliability under Radiation

et al. 2012

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“…The collection efficiency g represents a measure of the charge collected by the circuit node after an ion strike and will be discussed in Section 4.2. For simplicity, we will focus on strikes occurring in (or near) the drain of the off NMOSFET, which still accounts for most of the SEUs, even in scaled devices [17].…”

Section: Discussionmentioning

confidence: 99%

Temperature dependence of neutron-induced soft errors in SRAMs

Bagatin

Gerardin

Paccagnella

et al. 2012

Microelectronics Reliability

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a b s t r a c tWe irradiated commercial SRAMs with wide-spectrum neutrons at different temperatures. We observed that, depending on the vendor, the soft error rate either increases or slightly decreases with temperature, even in devices belonging to the same technology node. SPICE simulations were used to investigate the temperature dependence of the cell feedback time and restoring current. The shape and magnitude of the particle-induced transient current is discussed as a function of temperature. The variability in the response is attributed to the balance of contrasting factors, such as cell speed reduction and increased diffusion with increasing temperature.

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Innovative Simulations of Heavy Ion Cross Sections in 130 nm CMOS SRAM

Cited by 34 publications

References 14 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

Optimization of SEU Simulations for SRAM Cells Reliability under Radiation

Temperature dependence of neutron-induced soft errors in SRAMs

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