SEU Prediction From SET Modeling Using Multi-Node ... - IEEE Xplore

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SEU Prediction From SET Modeling Using Multi-Node Collection in Bulk Transistors and SRAMs Down to the 65 nm Technology Node Laurent Artola, Guillaume Hubert, Kevin M. Warren, Marc Gaillardin, Ronald D. Schrimpf, Robert A. Reed, Robert A. Weller, Jonathan R. Ahlbin, Philippe Paillet, Melanie Raine, Sylvain Girard, Sophie Duzellier, Lloyd W. Massengill, and Francoise Bezerra

Abstract—A new methodology of prediction for SEU is proposed based on SET modeling. The modeling of multi-node charge collection is performed using the ADDICT model for predicting single event transients and upsets in bulk transistors and SRAMs down to 65 nm. The predicted single event upset cross sections agree well with experimental data for SRAMs. Index Terms—Cross section prediction, heavy ion irradiation, multi-node collection, single event transient, single event upset, transient current model.

I. INTRODUCTION HE impact of radiation on electronic devices designed for space and atmospheric applications has been known for more than four decades [1]. The continuous shrinking of electronic device dimensions has resulted in significant changes that may affect radiation hardness. For instance, the contribution of charge sharing and Multiple-Bit Upsets must be considered to estimate the sensitivity of highly scaled technologies [2]–[5] to ionizing particles. To do so, various SEE prediction tools have been developed for modern electronics such as SEMM-2 from IBM [6], MRED [7]–[12] from Vanderbilt University, and MUSCA [2], [13]–[15] from ONERA. These tools share a similar approach based on the critical charge concept for SEU predictions.

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Manuscript received December 02, 2010; revised March 02, 2011; accepted April 13, 2011. Date of publication May 27, 2011; date of current version June 15, 2011. L. Artola is with ONERA, 31055 Toulouse, France and also with CNES, 31401 Toulouse, France (e-mail: [email protected]). G. Hubert and S. Duzellier are with ONERA, 31055 Toulouse, France (e-mail: [email protected]; [email protected]). K. M. Warren and L.W. Massengill are with the Institute for Space and Defence Electronics, Vanderbilt University, Nashville, TN 37203 USA (e-mail: [email protected]; [email protected]). M. Gaillardin, P. Paillet, M. Raine, and S. Girard with the CEA, DAM, DIF, F-91297, Arpajon, France (e-mail: [email protected]; [email protected]; [email protected]; [email protected]). R. D. Schrimpf, R. A. Reed, R. A. Weller, and J. R. Ahlbin are with the Electrical Engineering and Computer Science Department, Vanderbilt University, Nashville, TN 37235 USA (e-mail: [email protected]; [email protected]; [email protected]; [email protected]) F. Bezerra is with CNES, 31401 Toulouse, France (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TNS.2011.2144622

In this work, an updated physically-based transient approach, the Advanced Dynamic DIffusion Collection Transient (ADDICT) model, is applied to predict the SEE sensitivity of advanced CMOS technologies. ADDICT uses semiconductor physics parameters to calculate Single-Event Transient waveforms instead of the “double exponential“ classically used to describe such currents. ADDICT uses ambipolar diffusion and the dynamic carrier collection velocity to calculate SET waveforms. This approach allows use of an upset criterion based on physically-calculated SET waveforms, as well as the critical charge, to analyze single-event effects in advanced devices. The ADDICT methodology is first described for SET simulations. The ambipolar diffusion mechanism is described [16]. The main physical improvement provided by ADDICT, i.e., the dynamic collection velocity, is presented along with the multiple charge collection methodology, based on charge sharing rules as a function of the distance from the strike location to the collection volume, the local electric field and potential, and process parameters like the substrate/well doping. The ADDICT approach is validated against experimental and TCAD data on single transistors. SETs calculated with ADDICT are either compared to experimental measurements for and NMOS bulk transistors [17], or to 3D TCAD simulations based on calibrated process simulations for 90 nm and 65 nm technologies. ADDICT calculations are performed to investigate multiple-node charge collection using a structure containing 90 nm NMOS transistors, leading to a new upset criterion. This takes into account charge sharing phenomena. This criterion is used to calculate the heavy-ion SEU cross-section of SRAMs of various CMOS technologies. II. ADDICT METHODOLOGY FOR SET MODELING A. ADDICT Single-Event Transient Modeling Approach ADDICT calculates the SET response based on the underlying physics phenomena (field modulation, multiple-node charge collection, and ambipolar diffusion), while most event-rate simulators describe the current waveform using a double exponential [18], based on fitting parameters obtained from TCAD simulations. The calculations are based on technology parameters obtained from the ITRS Roadmap [3], adapted to an “end-user” approach. The ADDICT model is time-adapted to a Monte-Carlo approach of the SEE prediction problematic. Charge diffusion and collection after a heavy ion

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ARTOLA et al.: SEU PREDICTION FROM SET MODELING USING MULTI-NODE COLLECTION IN BULK TRANSISTORS

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carrier density inside the track due to carrier-carrier scattering [16]. captures the impact of the heavy The modeling of ion track on the electric properties of the depletion region. The mechanisms of the potential modulation and saturation are similar to the field-funneling model [16], [22]–[24] . The dynamic collection velocity is described by: (3)

Fig. 1. Charge collection principle used in ADDICT. The schematic shows a structure with 3 collection areas applicable to the multiple-node charge collection. The charge deposited along the heavy ion track is sampled into elemenbetween each elementary charges and evtary charges . The distance (drain of each transistor) under consideration is then calerycollection area culated taking into account the Shallow Trench Isolation (STI). Parasitic current are finally estimated. transients associated to each charge collection area

Q

I

A

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of the initial collection velocity where the attenuation depends on the location and LET of the heavy ion. At the depletion boundary, the collection velocity varies between and , due to velocity saturation [22], and the type of carrier (i.e., electrons or holes) is the ratio between [26]–[28]. The attenuation coefficient and , the sum the drain junction capacitance, of the equivalent capacitance of each elementary segment of the heavy ion:

A

(4) strike depend on the doping density and local electric field [16], [18] [19]. The bias voltage and doping determine the drain junction capacitance. B. Dynamic Charge Collection Velocity Implementation A spherical ambipolar diffusion model [16], [20] was first developed in ADDICT to describe the transport of radiationdeposited charges. This approach is accurate for relatively low electric fields and doping gradients but it suffers from inherent limitations in high field regions. In those cases, the dynamic collection velocity is used to describe the current: (1) is the node current, is the surface carrier denwhere is the dynamic collection velocity, and is the elesity, mentary charge. in the The temporal and spatial carrier concentrations silicon bulk are obtained by discretizing the ion track and the charge collection surfaces [16], [20] :

(2)

where is the ambipolar diffusion coefficient, is the distance between the track and the collection node(s) (depletion boundary [16]), and is the carrier life time. This takes into account recombination processes [21]. , between each elementary segment The distance, and surface (collection area, ) is calculated taking into account the Shallow Trench Isolation (STI) geometry and locations, as shown in Fig. 1. The charges are collected by the drain of each transistor, . The ambipolar diffusion coefficient is modeled with a dynamic approach, limiting based on the

where depends on the temporal and spatial carrier concentration calculated from (2), and on the range of the ion track obtained from SRIM [29]. has The determination of the initial collection velocity been described previously in [26]–[28]. It depends on the electric field in the depletion region under pre-strike steady state conditions. The electric field is defined by the depletion capacand the bias voltage [16]. The drain junction itance capacitance depends on the depletion-region thickness. The colfor a PMOSFET is lower than that for an lection velocity, NMOSFET due to the lower mobility of holes than electrons ranges [27], [28] . Thus, the drift velocity for electrons to and the drift velocity for holes from ranges from to [22]. III. VALIDATION OF THE ADDICT MODELING METHODOLOGY A. Comparison of ADDICT Calculated SETs: Worst Case Response Fig. 2 shows a direct comparison between current transient measurements obtained for NMOS transistors under heavy ion irradiation (filled red circles) [17] and ADDICT calculations. The transient current is induced by a 6.2 ion irradiation at GANIL at normal incidence MeV/u . The range of particles was obtained from SRIM [29]. The response shown for the transistor is the worst case, i.e., the largest amount of collected charges. The transient response has been modeled for the technology based on the technological assumptions described in [3], [17] and [30]. The shape of the ADDICT-calculated SET waveform (filled black squares) agrees relatively well with the measured transient response (filled red circles). As described in [17], the differences between calculations and experiments may be explained by the

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Fig. 2. Comparison of the transient current (filled symbols, left axis) and the drain collected charge (empty symbols, right side) obtained from ADDICT (black squares) and experimental measurements (red dots). The SET measurements have been induced by 6.2 MeV/u Ca ions with a LET of about 15 MeV:cm . mg at GANIL for a 0:18 m NMOS transistor.

IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 58, NO. 3, JUNE 2011

Fig. 4. Comparison of the current response calculated by ADDICT (black squares), calculated by “classic” diffusion model (red dots), and modeled by TCAD simulations (green triangles) The transient current pulse has been induced by a 10 MeV:cm :mg heavy ion at normal incidence for a 90 nm NMOS transistor.

capacitances are considered: the TCAD model ( for for 65 nm technologies) and two values 90 nm and bracketing the initial junction capacitances. Fig. 3 shows that ADDICT calculations give a good estimation of the worst case drain collected charge after a heavy ion strike for various technology nodes. This challenge has been performed with physical parameters obtained from the ITRS Roadmap [3] without any fitting. However, the collected charge does not provide insight into the dynamic collection velocity. B. Comparison of the Dynamic Charge Collection Velocity in 90 nm and 65 nm Technologies

Fig. 3. Overview of the ADDICT calculations of the drain collected charge for various technology nodes (black squares). The experimental measurements of the drain collected charge (red dots) are obtained from [17]. The SET currents measured have been induced by a 15 MeV:cm . mg heavy ion . The TCAD model is presented for the 90 nm and 65 nm NMOS device (green triangles).

limited bandwidth of the experimental setup [31]. This leads to underestimation of the amplitude and to overestimation of the duration of the measured signals without modifying the total collected charge (unfilled symbols on the right hand side of Fig. 2), which is calculated by the integration of the transient current. The drain collected charge is used as the metric to check the accuracy of the ADDICT calculations for various technology nodes. The total collected charge is usually used in prediction methodologies to estimate the SEE cross section [7]–[12] and [2], [13]–[15]. Fig. 3 exhibits the charge collected on the drain electrode of NMOS transistors biased in the OFF-state ( , other terminals grounded) struck by a 6.2 MeV/u ion (same as Fig. 2). The collected charge obtained from the experimental measurements [17], TCAD simulations and ADDICT calculations is compared in Fig. 3. The error bars reflect the impact of the depletion capacitance value used as an input parameter of the model, on the collected charge. Three junction

In order to validate ADDICT for integrated devices, the calculation of the SET currents was performed for 90 nm and 65 nm NMOS transistors. The dynamic collection modeling is strongly relevant to predict SET in highly integrated devices. The substrate profile is characteristic of 90 nm bulk techwith a nology. The P-type substrate doping is at a depth latchup mitigation deep implant of . n-well and p-well peak doping levels of approximately are [32]. The n-diffusion region is biased at 1.2 V. Each transistor is separated by STI. Fig. 4 shows the transient current response induced by a at normaheavy ion with a LET of about lincidence on a 90 nm NMOS transistor obtained by TCAD simulation (green triangles), calculated by ADDICT (black squares), and obtained from a “classic” diffusion model (red dots) [20], [34] . The improvement in the trend, with regards to the transient in term of maximum and decreasing time characteristics, using ADDICT is significant. The same comparison has been done for the 65 nm NMOS transistor in Fig. 5 for a heavy ion with an LET of at normal incidence. The “classic” diffusion model has been adjusted (diffusion constant and carrier velocity) for the 65 nm technology node [35]. The predicted SET current (filled black squares) is in excellent agreement with the TCAD simulation (filled green

ARTOLA et al.: SEU PREDICTION FROM SET MODELING USING MULTI-NODE COLLECTION IN BULK TRANSISTORS

Fig. 5. Comparisons of the transient current (left side) and the drain collected charge (right side) obtained by ADDICT (black squares), a “classic” diffusion model [20], [34] (red dots), and the TCAD model (green triangles) . The comparisons have been done for a single 65 nm NMOS transistor at off state stroke by a 5 MeV:cm :mg in the drain contact at normal incidence.

triangles) while the “classic” diffusion model (filled red dots) overestimates the response. The same level of agreement is obtained for the collected charge (empty symbols). The maximum value of the current and the shape of the waveform agree with the TCAD simulation better than the current obtained from the classic diffusion model. The improvement is mainly due to including the effects of the variation of the electric field and collection velocity inside the depletion region (in short-circuit case) while the “classic” diffusion model does not. The error bars for the ADDICT calculation show the impact of the depletion capacitance value on the collected charge. Three junction capacitances are considered here: the TCAD model [32], and two values bracketing the condition and . initial junction capacitances, C. Statistical ADDICT Calculations Vs. Experimental Measurements for NMOS Transistor To conclude the validation, transient measurements were recorded for different locations of the ion strike. The location of the ion strike is known with precision at the micro-beam. The results are summarized in Fig. 6, which shows a mapping of the drain collected charge vs. position along the source-drain NMOS transistor. The predicted axis for the same drain collected charge is relatively close to the measurements from [17] for all strike locations. The lowest collected charge estimated by ADDICT cannot be measured by the experimental setup because the detection threshold is too high. The methodology used for SET prediction demonstrates an improved estimation of the transient behavior of electronic devices under heavy ion irradiation. After this validation of elementary currents calculated for single transistors, ADDICT is used to model multi-node in the following section. IV. MODELING OF THE MULTI-NODE CHARGE COLLECTION IN ADDICT To investigate the accuracy of the multi-node charge collection in the ADDICT tool, TCAD simulations were performed for a structure containing three NMOS transistors, each having the same characteristics as the 90 nm transistor described in the

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Fig. 6. Comparison of the mapping of the drain collected charge as a function of the ion strike location along the source—drain axis of a 0:25 m NMOS transistor. The experimental measurements of the drain collected charge (empty black symbols) [17] and the drain collected charge calculated by ADDICT (blue symbols) are obtained for a 35 MeV/u Cl (normal incidence) with an LET of about 15 MeV:cm :mg at Sandia.

Fig. 7. Schematic of multiple NMOS transistor of the TCAD structure. Simulations were performed for a range of tilt angles of heavy ion, from normal incidence (90 ) to grazing incidence (0 and 180 ). The calculations have been done for a 10 MeV:cm :mg heavy ion with a range of 15 m through the drain at off state of the NMOS1.

previous section, as shown in Fig. 7. The STI has a depth of and a width of [32]. The STI reduces the maximum value of the current peak due to containment of the ion-generated charges and an increase in the dis-tance from the heavy ion track to the collection zone. Fig. 8 shows the drain collected charge of each NMOS transistor compared to TCAD simulations, ADDICT, and the classic diffusion model [20], [34], [35]. The comparisons have been heavy ion with a range of done for a through the drain of an off-state device for various tilts. The dynamic modeling of the ambipolar diffusion coefficient and of the collection velocity in ADDICT provide a means to better calculate (slightly overestimated) the total collected charge (black squares), except for 90 and 60 tilts with the NMOS1 transistor, as shown in Fig. 8 (1). On the other hand, the classic diffusion model (red dots) using static physical parameters (diffusion constant and carrier velocity [34], [35]), overestimates the collected charge for all tilt

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Fig. 9. Schematic of a 6T SRAM (a), and a schematic of SRAM showing by both inverters, i.e., Node 1 and Node 2 (b). The both NMOS lateral transistors represent the pass-gate transistors which allow for the read and write process.

triangles) and ADDICT (black squares) are due to recombination (Fig. 8 (2)) while the classic diffusion model (red dots) does not include this mechanism. The differences between the ADDICT and TCAD comparisons of the collected charge by the adjacent nodes, as shown in Fig. 8 (2) and (3) are due to simplifications of the compact model, specially in diffusion cases. The full substrate processes are not taken into account, and the collection mechanisms of the source are not included. An important point to demonstrate in Fig. 8, is the difference of the total calculation time needed to obtain these data for TCAD and ADDICT simulations. The TCAD model needs a total simulation time of approximately 15 hours, while ADDICT requires (including error bar calculations) less than 15 seconds (for all ion events). The ADDICT model is time-adapted to a Monte-Carlo approach of the SEE prediction problematic. This investigation of multi-node charge collection represents a step forward for the validation of the ADDICT model and its application to the problem of modeling the effects of charge collection at multiple devices. The last step of this work consists to use these currents to predict SEU in SRAM devices.

V. ADDICT METHODOLOGY DEDICATED FOR THE SEU CROSS SECTION PREDICTION A. A New Upset Criterion With ADDICT Applied To Multi-Collection and Charge Sharing Fig. 8. Comparison between TCAD simulation (black squares), “classic” diffusion model [20], [34] (red dot) and ADDICT tool (green triangles) of the Drain collection charge for each NMOS transistor: NMOS1 (1), NMOS2 (2), NMOS3 (3). A range of tilt angles are considered for heavy ion tracks, from normal incidence (90 ) to grazing incidence (0 and 180 ). The calculations have been done for a 10 MeV:cm :mg heavy ion with a range of 15 m through the drain at off state of the NMOS1.

angles by one-two orders of magnitude, depending on the transistor. The smaller collected charge obtained by TCAD (green

Upset criteria for ions that do not strike the drain (diffusiondominated events) are typically based on the ratio of the peak [20]. In this section, current and transient duration a new and unique upset criterion is presented to be included in the calculated currents for SRAM devices for the whole configurations of the ion track. The model includes the feedback loop of the circuit. It was previously shown that coupling of the N and P transistors in the “on” state may cancel the flipping process before an upset occurs [36]–[38].

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TABLE I SUMMARY OF TECHNOLOGICAL DESCRIPTIONS AND CRITERIA UPSET FOR THE SRAMS USED IN ADDICT FOR THE SEU CROSS SECTION PREDICTION. ALL SRAM DEVICES ARE IN BULK TECHNOLOGY. THE TECHNOLOGICAL NODE OF THE DEVICE STUDIED SCALES FROM 0:25 m TO 65 NM

The “upset” currents, both inverters:

, are described by (5)–(6) for

(5) (6) The current of both inverters must be modeled to take into account the coupling mechanisms occurring in the SRAM cell. The parameter á represents the effect of the circuit delay between the inverters. It allows one to adjust the feedback loop during the evolution of the upset process. It has a value of approximately 0.1–0.2, depending on the circuit design and the technology node. , are used to calculate the maxThe transient currents, , which is compared with the critical charge imum charge, . If of one of the inverters is greater than , an upset (SEU) occurs. This calculation accounts for the characteristic time, , representative of the effective collection time required to upset the SRAM cell, as described in (7).

(7)

is deduced from the maximum value of the curve . The integration time, , is a physical fitting parameter, which is the order of picoseconds, depending on the technology node [9], [18], [39], [40]. This characteristic time is close to the gate delay corresponding to a fan-out of 1 [39]. Using a short integration time results in an underestimation of the maximum collected charge, and consequently in the SEU cross section as well. B. ADDICT Calculations Vs. Experimental SEU Cross Sections The predicted cross section for single event upset is calculated by dividing the number of errors by the total number of events. ADDICT was used to calculate a SEU cross section for various technology nodes using the characteristics summarized in Table I. The predictions were performed for SRAM memories with and 65 nm. The parameters of the gate lengths between upset criterion follow the roadmap trend given by ITRS, foundry and ground tests, as shown in Table I.

Fig. 10. Comparison of the experimental SEU cross section measurements and ADDICT predictions for 0:25 m SRAMs: (a) Overview of the global trend of ADDICT prediction covering the SEU cross section measured for 3 different founders. (b) Detailed contribution of the global ADDICT prediction depending on process (depletion capacitance) and on upset and circuitry parameters (critical charge, integer time) . Error bars calculated by ADDICT have been performed for 20% of input parameters. The testing for the 0:25 m SRAM from Cypress [47] and from IBM [48] were carried out at LBNL 88’ cyclotron and at TAMU cyclotron facility. The testing for the 0:25 m SRAM from Atmel [49] were carried out at IPN and at the UCL cyclotron.

The comparisons of the SEU cross section between the experimental measurements and the ADDICT predictions for SRAMs are depicted in Fig. 10. These experimental parts from Cymeasurements were performed for press [47], from IBM [48] and from Atmel [49] at Lawrence Berkeley National Laboratory (LBNL), Texas A&M University (TAMU), the Nuclear Physics Institute (IPN) and at the UCL facility. The wide spread of experimental data emphasizes the impact of process and layout differences on the SEU cross section for the same technological node. The aim of this comparison is to reveal the robustness of the model using “ITRS input parameters” to capture the sensitivity of memories built by various founders. Fig. 10(a) demonstrates good predictive capability for SEU SRAMs (blue dots) using the cross sections for the usual fabrication process [3], [43], [47] validated on transient currents as presented in the previous session. The LET threshold

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Fig. 12. Comparison of the experimental SEU and MBU cross section measurements and ADDICT predictions for 65 nm SRAMs cell from Xilinx [52]. The experimental data has been collected for heavy ion irradiation at the K500 Cyclotron at TAMU and at the 88’ Cyclotron facility at LBNL.

Fig. 11. Comparisons of the experimental SEU cross section measurements and ADDICT predictions for various SRAM memories: The testing for the 0:18 m SRAM device from NEC (a) were carried out at the Lawrence Berkeley national Laboratory (LBNL) 88’ cyclotron with 4.5 MeV/a and 10 MeV/a heavy ion beams and at the Texas A&M University (TAMU) cyclotron facility using 25 MeV/a ions [47]. The heavy ion testing experiments for the 90 nm SRAM memory from Xilinx (b) were performed at the Cyclotron Institute, TAMU [51].

and the SEU cross section saturation at high LET calculated by ADDICT fit with the experimental data due to the good acknowledge of the device characteristics. No SEU were measured at or below for all SRAMs of each foundry but one SEU was considered that explains the lowest value in the depicted data. The global SEU cross section predicted (gray area) represents the maxtolerance of the ADDICT imum coverage obtained for a input parameters. The predictive coverage reveals the relevant process and layout description used in the approach to capture the essential physics driving the sensitivity of the SRAMs for different fabrication processes. The calculated SEU cross sections were convolved with the differential flux modeled for GEO orbit [50] and with 100 mils of shielding [48] to calculate the single event rate (SER), which was compared with the monthly average upset rate from in-flight data [48]. The calculation given by ADDICT (blue dots) is approximately

upsets/bit -day while the range of on-orbit SER is to upsets/bit-day. The estimated range obtained from to upsets/bit-day. These reADDICT is sults support the robustness of the approach used in ADDICT. variation of The error bars in Fig. 10(b) correspond to the input parameters. At low LET, the major contribution is provided by the process variations (gold error bars), i.e., depletion capacitance, while in saturation the variations of circuitry and upset criteria (blue error bars) represent 90% of the variation. The results presented in Fig. 10 demonstrate the predictive capability for SEU cross section based on the key technology parameters, but without a layout description. We now consider quantitative ADDICT predictions based on a full device description. SRAM memory from The SEU cross section for a NEC is depicted in Fig. 11(a). The testing was performed at the Lawrence Berkeley National Laboratory (LBNL) 88’ cyclotron with 4.5 MeV/u and 10 MeV/u heavy ion beams and at the Texas A&M University (TAMU) cyclotron facility using 25 MeV/u ions [47]. The LET SEU threshold is predicted accurately, while the saturated cross section diverges from the measurements at . for the saturation, i.e., SRAM. The error bars correspond to variation of the ADDICT input parameters. The comparison between the cross section calculated by ADDICT and the measured SEU cross section [51] for a 90 nm SRAM from Xilinx is shown in Fig. 11(b). The heavy ion experiments [51] were performed at the Cyclotron Institute, TAMU. The SEU cross section was calculated in the FPGA configuration. The SRAM design has NMOS devices with drain areas that are four times greater than those of the PMOS devices in terms of surface area [46]. The cross section prediction agrees with the experimental measurements but with a slight overestimation for all LETs except at saturation. Up to the 90 nm technology node, the cell scaling followed the classic rules. For the 65 nm node and smaller, however, the scaling rules have changed [13], [39], which has a strong impact on the sensitivity of the SRAM devices.

ARTOLA et al.: SEU PREDICTION FROM SET MODELING USING MULTI-NODE COLLECTION IN BULK TRANSISTORS

The cross section for a 65 nm SRAM from Xilinx (in FPGA configuration) is compared in Fig. 12, as obtained from experimental measurements and ADDICT calculations. The experimental data were obtained for heavy ion irradiation at the K500 Cyclotron at TAMU and the 88” Cyclotron facility at LBNL for [52]. a LET range of 1.3Fig. 12 shows the accuracy of the upset threshold and the saturation value. As previously discussed, the error bars have variation of the input parameters. been obtained for Multiple bit upsets (MBU) were measured [52], and represent 60% of the total SEU cross section at maximum LET , while the estimation of the MBU rate is about 55%. A good modeling of the multi-collection phenomenon, thanks to a detailed acknowledge of the SRAM topology, improves the predictive calculations. This last SEU prediction demonstrates the improved predictive capabilities of the model and general methodology for highly integrated devices. VI. CONCLUSION This paper describes a simulation tool dedicated to the modeling and prediction of single-event upsets in highly integrated devices, such as SRAMs at and below the 65 nm technology node. ADDICT models the single-event transient current response using a physics phenomenology, with input parameters issue from ITRS trends, whereas double exponential model, and classic diffusion model use fitting parameters only based on preliminary TCAD simulations. Good agreement between the predicted transient currents, experimental data and TCAD simulations is demonstrated. The criticality of using a transient current model because of the scaling of device dimensions has been revealed. The simulation tool has a significant speed advantage compared to TCAD simulations (more than 3 orders of magnitude). A new single event upset criterion associated with ion induced transient currents provides predictive ability with respect to SEU cross sections. Comparisons with experimental SEU measurements show the accuracy of ADDICT over a wide range of technology nodes. This work also will facilitate the investigation of single event transients in combinational logic . REFERENCES [1] D. Binder, E. C. Smith, and A. B. Holman, “Satellite anomalies from galactic cosmic rays,” IEEE Trans. Nucl. Sci., vol. NS-22, no. 6, pp. 2675–2680, Dec. 1975. [2] G. Hubert, S. Bourdarie, L. Artola, S. Duzellier, C. Boatella-Polo, F. Bezerra, and R. Ecoffet, “Impact of the solar flares on the SER dynamics on micro and nanometric technologies,” IEEE Trans. Nucl. Sci., vol. 57, no. 6, pp. 3127–3134, Dec. 2010. [3] International Technology Roadmap for Semiconductor, 2006. [4] C. W. Gwyn, D. L. Scharfetter, and J. L. Wirth, “The analysis of radiation effects in semiconductor junction devices,” IEEE Trans. Nucl. Sci., vol. NS-14, no. 6, pp. 153–169, Dec. 1967. [5] P. E. Dodd, F. W. Sexton, G. L. Hash, M. R. Shaneyfelt, B. L. Draper, A. J. Farino, and R. S. Flores, “Impact of technology trends on SEU in CMOS SRAMS,” IEEE Trans. Nucl. Sci., vol. 43, no. 6, pp. 2797–2804, Dec. 2004. [6] H. H. K. Tang and E. H. Cannon, “SEMM-2: a modeling system for single event upset analysis,” IEEE Trans. Nucl. Sci., vol. 51, no. 6, pp. 3342–3348, Dec. 2004.

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