Calculating Spacecraft Single Event Environments ...

Calculating Spacecraft Single Event Environments with FLUKA: Investigating the effects of spacecraft material atomic number on secondary particle showers, nuclear reactions, and linear energy transfer (LET) spectra, internal to spacecraft avionics materials, at high shielding mass Steve Koontz Member IEEE, Brandon Reddell, and Paul Boeder Abstract--The contribution, to spacecraft avionics single event effect (SEE), nuclear reaction (NR), and total ionizing dose (TID) environments, of space radiation induced nuclear reactions and secondary particle showers in spacecraft materials is explored using the FLUKA Monte Carlo energetic particle transport code. Estimates of spacecraft single event upset rates produced using FLUKA based methods are compared to flight data. The elemental composition of avionics devices is shown to affect device SEE, NR, and TID environments as does the composition of the spacecraft structural shielding mass. I.

INTRODUCTION

galactic cosmic rays, as well as particulate Sradiationandtrapped in planetary magnetospheres, interact OLAR

with spacecraft materials to produce: 1) ionization along the particle track caused by electromagnetic interaction with electrons in the spacecraft material, 2) secondary particle showers that result from high energy collisions between cosmic ray nuclei and the atomic nuclei in spacecraft materials, and 3) displacement damage to the crystal structure of some materials resulting from lower energy collisions [15]. The character of space radiation interactions with spacecraft materials depends strongly on both the amount and composition of shielding mass and the elemental composition and atomic number of micro device materials [5-8]. Effects of nuclear reactions in spacecraft materials are of interest for both manned and unmanned space flight. Recent rapid changes in the elemental composition of microelectronic devices, with increasing use of high atomic number elements such as silver (Ag), tungsten (W), hafnium (Hf), and lead (Pb), is one motive for the increasing interest in the application of Monte Carlo nuclear reaction and transport ________________________________________________ Manuscript received June 20, 2011. Steven Koontz is with NASA Johnson Space Center, Mail Code ES4, 2101 NASA Parkway, Houston, Texas, USA 77058 (telephone: 281-4838860, e-mail [email protected]) Brandon Reddell with NASA Johnson Space Center, Mail Code EV5, 2101 NASA Parkway, Houston, Texas, USA 77058 (telephone: 281-4835050, e-mail [email protected]) Paul Boeder is with the Boeing Company, 13100 Space Center Blvd. HB3-20, Houston, Texas, USA 77059 (telephone 281-226-6736, e-mail [email protected])

models [9-11] to understanding and predicting the spacecraft SEE environments that are important for both manned and unmanned space flight. Nuclear reactions and secondary particle showers also play an important role in determining the effectiveness of spacecraft structural and shielding materials [9-11] and contributing to understanding and predicting the spacecraft SEE environments that are important for both manned and unmanned space flight. Nuclear reactions and secondary particle showers also play an important role in determining the effectiveness of spacecraft structural and shielding materials in meeting astronaut radiation exposure limits during long duration missions outside Earth’s magnetosphere [12, 13]. The magnitude of secondary particle showers increase with the kinetic energy of the cosmic ray primary, the atomic number of the cosmic ray primary, and the atomic number of spacecraft materials [14, 15]. The most abundant hadronic components of cosmic ray secondary particle showers, i.e. protons, neutrons, and pions, can trigger further nuclear reactions in microelectronic device materials [14, 15], producing short range, high linear energy transfer (LET) nuclear reaction fragments. The amount of ionization along the particle track and the corresponding likelihood of a single event effect depend on the particle LET. Particle LET depends on particle velocity and particle atomic number squared (Z2) [9-11] so that high Z element fission fragments and spallation products are expected to present an increased risk of SEE damage in devices containing high Z elements. In this paper we report the results of FLUKA (FLUktuierende Kaskade) [16] Monte Carlo energetic particle transport code calculations of spacecraft microelectronic device SEE, TID, and NR environments and how the elemental composition of spacecraft structural and avionics materials affect those environments. First, FLUKA [16] calculations of spacecraft avionics SEE and corresponding single event upset (SEU) rates are compared with in-flight data for several spacecraft avionics devices as a first step toward validating the FLUKA code for spacecraft SEE applications. Second, CREME-96 [17] deterministic modeling of SEU rates and the Petersen Figure of Merit (FOM) [18, 19] estimates are also compared to FLUKA SEU rate estimates

and in-flight data, to further validate our FLUKA based methods against widely used semi-empirical methods for those cases in which all three methods are applicable. Third, FLUKA is used to evaluate the effect of thin heavy element metallization layers on the SEE environment in the underlying thin silicon (Si) micro device layers. Finally, FLUKA is used to examine the effects of spacecraft structural shielding mass composition (aluminum (Al) vs. polyethylene (PE)) and thickness on the SEE environments in thin Si micro device layers. II. METHODS Natural space radiation environment definitions used in FLUKA calculations of the International Space Station (ISS) and geosynchronous/interplanetary ionizing radiation environments (default 1977 solar minimum input spectra for both GCR and trapped radiation) were obtained from the CREME-96 Web Page [20]. In the interest of increasing computational efficiency with minimal effect on the accuracy of the result, only the 8 most abundant GCR elements (H, He, C, O, Mg, Si, and Fe) that comprise more than 90 percent of the total GCR flux were used in the FLUKA simulations. Fully three dimensional FLUKA simulations were conducted on a generic concentric spherical shell “spacecraft” model with thin layers (10 μ) of Si “scoring” detector shells placed between layers of aluminum or polyethylene shielding mass and labeled SiDet1 to SiDet8 from thinnest to thickest shielding as shown in Table 1. The inside diameter of the spherical shell structure is 100 meters. The interior of the sphere is treated as a perfect particle absorber. Each scoring detector shell then has a well defined shielding mass distribution function and median shielding mass, calculated while looking outward from the Si detector shells. Thin (1 μ) layers of metallization, e.g. Ag, W, Hf, Pb, or other heavy elements can be added to the outward facing surface of the silicon detector shells to determine the effect on SEE environment internal to the Si detector shell. The 10 µSi scoring shells with 1µ metallization layers constitute a simple geometric and compositional model of a generic microelectronic device for the purposes of SEE, NR, and TID calculations using FLUKA. FLUKA is a fully integrated, extensively verified, and widely utilized Monte Carlo simulation package for the interaction and transport of high energy particles in matter, including explicit physics based energy loss and ionization, hadron-nucleus and nucleus-nucleus collisions, and fully developed, complete secondary particle shower production and shower induced nuclear reactions [16]. The FLUKA code is based on proven physical theory and extensively benchmarked against accelerator and other high energy nuclear transport and reaction data, not empirical or semiempirical look-up tables. FLUKA versions 2008.3b and 2008.3c were used in the work reported here. The “Precision” default settings, the Relativistic Quantum Molecular Dynamics (RQMD), and Dual Parton Model with Jets (DPMJET) nuclear collision models, Evaporation, Coalescence, and Electromagnetic Dissociation are all enabled, as is the “PEANUT” (Glauber-Gribov and

generalized intra nuclear cascade) hadronic interactions model, over the entire projectile energy range [16]. FLUKA randomly samples the natural particle spectra appropriate for the specific flight environment and fires randomly directed particles into the concentric spherical shell structure from the outside, so as to simulate an isotropic (International Commission on Radiation Units (ICRU) definition) particle flux incident on the model spacecraft. For each particle fired into the spacecraft, FLUKA calculates the following through the thickness of the concentric shell structure: 1) energy loss/deposition (LET along the particle tracks) of primary and secondary particles, 2) nuclear reactions and reaction products (secondary particle showers), and 3) both energy loss and further nuclear reactions in the secondary shower particles. The result is the differential LET spectrum entering each of the 10μ Si detector shells which includes all contributions from both primary and secondary particles formed in shielding and in any thin heavy element layer proximal to the Si detector shells (i.e. inside the simple geometric model of a generic microelectronic device structure). The LET spectrum is recovered and reported using the FLUKA “USRYIELD” utility. FLUKA also reports the number of nuclear reactions in each specific region in the concentric spherical shell spacecraft model as defined in the FLUKA geometry input file. The regions are the structural shielding mass shells, the silicon detector shells and the metallization layers. Nuclear reactions caused by protons, neutrons, and pions, as well as the total ionizing dose to each defined region (i.e. shell or layer) using the FLUKA “SCORE” utility [16]. FLUKA user utilities are described in the FLUKA on-line manual [16]. The FLUKA differential LET spectrum, f(LET), for each Si detector shell is convoluted with the microelectronic device directional cross section, σ(LET, θ, φ), as derived from analysis of ground based heavy ion test data fitted to the Weibull cumulative distribution function, to produce the predicted on-orbit upset rate using established methods [2]. SEU Rate = ∫∫∫ f(LET) x σ(LET,Ɵ, Φ) d(LET)d(Ɵ) d(Φ) [2] In FLUKA and CREME calculations, σ(LET, θ, φ) is represented as a simple geometric volume having a total normal incidence area equal to σ(LET, 0o, 0o) and an aspect ratio (width/thickness) estimated from the test data when the test data is adequate to support the estimate. In many cases, the test data isn’t adequate to support direct estimation of the aspect ratio which is then selected for best agreement with the flight data. FLUKA based calculations utilize the simple cosine law, isotropic target, or right circular cylinder (RCC with median chord length only [21]), sensitive volume geometries. CRÈME-96 calculations are made with the rectangular parallel piped (RPP) sensitive volume geometry with the detailed chord length distribution function. Heavy ion test Weibull parameters are used directly for FOM calculations [21]. The same device parameters based on heavy ion testing as well as the same device sensitive volume

Table I FLUKA CONCENTRIC SPHERICAL SHELL MODEL SHIELDING MASS METRICS FOR Si DETECTOR SHELLS SiDet1 0.1 0.14 0.15

SiDet2 0.5 0.70 0.81

aspect ratios are used in all cases (Appendix, Table I). Monte Carlo models simulate real physical experiments or measurements including natural (random) quantum and statistical fluctuations, so the results of two statistically independent runs are not expected to be equal. As is the case for radioisotope decay, and other Poisson processes, the natural uncertainty in a Monte Carlo particle or event count is equal to the square root of the number of particles or events in the result. In the following, the size of the plot symbols is always selected to be larger than the expected uncertainty or error in the data points plotted. Sources of true experimental error, e.g. systematic and random errors in the heavy ion test data, are not treated explicitly. III. RESULTS AND DISCUSSION 1: COMPARISON OF FLUKA, CREME AND FOM SEE RATE ESTIMATES WITH IN-FLIGHT DATA Fig. 1 compares on-orbit SEU rates for various CMOS spacecraft micro-devices with the values estimated using the FLUKA Si shell LET spectra. On-orbit SEU rate estimates produced with the Peterson Fig. of Merit [FOM] and the CREME-96 are also shown in Fig. 1 for purposes of comparison. The data plotted in Fig. 1 are shown in the Appendix, Table II. As can be seen in Fig. 1, the three SEU rate estimation methods provide comparable accuracy, are generally within a factor of 10 of the on-orbit rate, and often much better. As shown in Table II, FLUKA provides an accurate prediction of the effects of shielding mass on SEU rates at 10 g/cm2 and 40 g/cm2 Al mass for two CMOS DRAM memory devices. The three SEU rate estimators are compared below using a least squares metric.

SiDet3 1.0 1.40 1.60

SiDet4 5.0 6.90 7.90

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FLUKA, FOM , CREME SEU rate - SEU/(bit day)

Shielding mass metric- g/cm2 Al Minimum Median, cosθ correction only Median, cosθ and solid angle view factor correction

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SiDet5 10.0 13.7 15.6

SiDet6 20.0 27.3 31.1

SiDet7 50.0 68.1 77.5

SiDet8 100 137.2 156.2

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In-Flight SEU rate - SEU/(bit day) Fig. 1. Comparing FLUKA, CREME and FOM SEU rate estimates with inflight SEU rates TABLE II SHIELDING MASS EFFECTS ON SEU RATES (SEU RATE AT 10 g/cm2 /(SEU RATE AT 40 g/cm2), Al SHIELDING, ISS FLIGHT ENVIRONMENT Device

Rate Ratio In-Flight

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TI (1M x 4) TMS44400 TI (4M x 4) TI SMJ41640

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IV. RESULTS AND DISCUSSION 2: EFFECTS OF HEAVY ELEMENT LAYERS ON NUCLEAR REACTION RATES AND THE

LET SPECTRA ENTERING THE SI DETECTOR SHELLS

However, the magnitudes of the least squares metrics are determined primarily by only four of the devices: 1) Mercury Messenger SRAM, 2) SMJ416400, 3) CP65656EV, and 4) PD4464D. On removing these four devices from the data set, the least squares metrics are dramatically improved, as shown below.

The accuracy of FLUKA predictions of Mercury Messenger SRAM SEU rates improves dramatically when a 1 micron W film is placed in contact with the outer surface of each Si detector shell, an expected outcome given the probable role of W in Mercury Messenger SRAM SEU sensitivity demonstrated earlier by Reed et al [32]. FLUKA simulations show that the magnitude of the W film effect can increase high threshold (> 10 (MeV cm2)/mg Si) device SEU rates at higher shielding mass by as much as three orders of magnitude. Fig. 2 (10μ Si shells only) and Fig. 3 (1μ W/10 μ Si) show a steady increase in the high (> 10 (MeV cm2)/mg Si) LET particle flux for the W metallization layer case, compared to the Si only case, with increasing shielding mass. As shown in Fig. 4 the increase in high LET particles entering the Si detector shells at high shielding mass increases with the atomic number of the 1 μ metallization layer as does

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(Z)^2/A, The Fissility Parameter Fig. 4. High (>10 MeV cm2/mg Si) LET particle flux into the 31 and 77 g/cm2 10μ Si detector shells as a function of the metallization layer fissility factor, Z2/A. Experimental fission probability is also plotted against Z2/A. Particle flux and fission probability numbers are normalized to W = 1 for more direct comparison - GEO/Interplanetary Environment

V. RESULTS AND DISCUSSION 3: STRUCTURAL SHIELDING MASS EFFECTS (AL VS.PE) COMPOSITION EFFECTS ON THE NUCLEAR REACTION RATES AND SI DETECTOR SHELL LET SPECTRA,

100

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Fig. 2. Particles/(cm2 day) with LET > X vs. median Al shielding mass GEO/Interplanetary Environment - Si detector shells only (no metallization layer) – particle LET values from top to bottom of graph are: 0.001, 0.012, 0.120, 1.30, 4.03, 9.8, 15 and 30 MeV cm2/mg (Si)

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TABLE III METALLIZATION LAYER ELEMENTS AND CORRESPONDING FISSILITY FACTORS AND FISSION PROBABILITIES AS PLOTTED IN FIG. 5 Element Ag Hf W Pb

# Particles (>10 LET units)/(sq cm day) or fission probability

# Particles per sq. cm per day (LET>X)

the probability of nuclear fission of an energetically excited nucleus [22-25]. The fissility parameters, Z2/A, and relative fission probabilities of the metallization layer elements, are shown in Table III. The number of high LET (> 10 MeV cm2/mg (Si)) particles entering the 10μ Si shells at median shielding masses of 31 and 77 g/cm2 Al correlates with the atomic number of the 1μ, high Z element metallization layers through the fissility parameter, Z2/A. Fission probability [2225] is at a minimum near Z2/A = 40 and increases for both larger and smaller atomic numbers. However, lower Z elements like Al are unable to produce significant yields of fragments having LET values > 10 MeV cm2 /mg (Si).

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Fig. 3. Particles/(cm2 day) with LET > X vs. median Al shielding mass GEO/Interplanetary Environment - 1μ W on 10 μ Si detector shells – Particle LET values same as Fig. 3

Fig. 5 and Fig. 6 show the FLUKA estimates for total ionizing dose, in cGy per day, to the Pb metallization layer (Fig. 5) and the Si detector shells (Fig. 6) when the shielding mass is Al or PE. The concentric spherical shell spacecraft model and the GEO interplanetary environment are used in the FLUKA simulations. Fig. 7 (Pb) and Fig. 8 (Si) show the corresponding number of nuclear reaction per cm3 per day caused by protons, neutrons and pions. Inspection of Fig. 5 and Fig. 6 reveals that the total ionizing dose to both the Pb metallization layer and underlying Si shell is reduced significantly by changing the shielding mass from Al to PE at all shielding mass values. In Fig. 7 and Fig. 8, the number of nuclear reaction per cm3 per day is clearly less for the Si shells than the Pb layers for both shielding mass cases as is expected from the relative nuclear reaction cross sections for the two elements. Changing the shielding mass from Al to PE produces a substantial reduction

in the nuclear reaction rate in the Pb layers but only a modest reduction in the nuclear reaction rate in the underlying Si shells. Neutrons and protons dominate the nuclear reaction rate at higher shielding mass values for both the Si shells and the Pb layers and neutrons drive the Pb rate at higher Al shielding mass values. Pions, while present, produce a relatively small contribution to nuclear reaction rates in both the Si shells and the Pb layers for both shielding mass cases.

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Fig. 5. Total ionizing dose, in cGy/day, to the 1 μ Pb metallization layer over a 10 μ Si detector shell as a function of median shielding mass for both PE and Al shielding mass – GEO/Interplanetary Environment

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Fig. 7. The number of nuclear reactions per cm3 per day in the 1 μ Pb metallization layers over 10 μ Si detector shells as a function of median shielding mass for both PE and Al shielding mass in the GEO/Interplanetary Environment. The nuclear reactions are induced by: top – Protons, middle – Neutrons, bottom - Pions

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VI. SUMMARY AND CONCLUSIONS 0.06 DoseTotAlSi j DoseTotPESi j 0.04

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Fig. 6. Total ionizing dose, in cGy/day, to the 10 μ Si detector shell under a 1 μ Pb a metallization layer as a function of median shielding mass for both PE and Al shielding mass – GEO/Interplanetary Environment

Using the methods and approximations described here, the FLUKA Monte Carlo nuclear reaction and transport code produces estimates of in-flight LET spectra and corresponding SEU rates that are in reasonable agreement with spacecraft in-flight data. FLUKA based SEE rate estimates are comparable with CRÈME-96 and FOM estimates for historical and contemporary spacecraft, both in LEO and in the GEO/Interplanetary environments. Shielding mass effects are most accurately described with the FLUKA based methods. FLUKA methods enable the evaluation of elemental composition effects inside microelectronic devices and how composition can affect LET spectra, total ionizing dose and nuclear reaction rates. Placing a (1 μ) high Z element (metallization layer) in close proximity to a thin (10 μ) Si layer can greatly increase the flux of high LET (LET > 10

MeVcm2/mg) particles into the Si layer but has little or no effect on the flux of low LET (LET < 10 MeVcm2/mg) particles into the Si layer. 1×10

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VII. REFERENCES [1]

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containing high Z (e.g. Hf, W, Pb) elements, independent of device SEU threshold. FLUKA methods enable evaluation of elemental composition effects in spacecraft structural shielding materials (Al vs. PE) on the LET spectra, TID and nuclear reaction rates in the shielded model microelectronic device layers. Reducing structural shielding mass average atomic number and increasing hydrogen content ( i.e. going from Al to PE) dramatically reduces the predicted ionizing dose rate, in cGy/day, as well as the nuclear reaction rate in both Si shell and Pb metallization layers. The flux of high LET particles, the TID and nuclear reaction rates in both the Pb metallization layer and the Si detector shell are dramatically reduced when PE replaces Al. Replacing Al with more PE like materials should mitigate heavy element effects in microelectronic devices while reducing the TID rate.

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AlmedianCOSΘvf j , PEmedianCOSΘvf j Fig. 8:The number of nuclear reactions per cm3 per day in the 10 μ Si detector shells under 1 μ Pb a metallization layers as a function of median shielding mass for both PE and Al shielding mass in the GEO Interplanetary Environment. The nuclear reactions are induced by: top – Protons, middle – Neutrons, bottom – Pions

The increase in high LET particle flux correlates with the fissility parameter, Z2/A, of the high Z element suggesting that many of the high LET particles result from fission of energetically excited high Z nuclei. The largest increases in high LET particle flux are predicted to occur at relatively high (between 10 and 100 g/cm2) shielding mass implying fission of the high Z elements by secondary shower particles, especially neutrons. Order of magnitude increases in particles with LET 30 or greater at high shielding mass imply an important increase in latch-up risk for labile components,

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[33] [34]

[35]

Space Station: performance vs. expectations for avionics and materials,” IEEE Radiation Effects Data Workshop, 2005, pp 110-116 Koontz, S., Reddell, B., Boeder, P.; “Using FLUKA to Calculate Spacecraft single Event Effects Environments: A Practical Approach,” Microelectronics Reliability and Qualification Workshop, Aerospace Corporation, December 8-9, 2009, Manhattan Beach California, http://www.aero.org/conferences/mrqw/ Falguere, D., Duzellier, S., Ecoffet, R., Tsourilo, I; “EQEX I-IV: SEE In-Flight Measurement on the MIR Orbital Station,” IEEE Radiation Effects Data Workshop Proceedings, pp89-95, 2000. Our thanks to Dave Bullington and Gayle Echo Thayer of Sandia National Labs for providing MISSE-7 FPGA SEU flight and ground test data O’Neill, P., Badhwar, G.; “Single Event Upsets for Space Shuttle Flights of New General Purpose Computer Memory Devices,” IEEE Transactions on Nuclear Science, 41(5), October 1994, pp 1755-1764 Hansen, D.L.; Jobe, K.; Whittington, J.; Shoga, M.; Sunderland, D.A, “Correlation of Prediction to On-Orbit SEU Performance for a Commercial 0.25-μm CMOS SRAM,” IEEE Transactions on Nuclear Science, 54(6), December 2007, pp 2525-2533 Reed, R.A.; Weller, R.A.; Mendenhall, M.H.; Lauenstein, J.-M.; Warren, K.M.; Pellish, J.A.; Schrimpf, R.D.; Sierawski, B.D.; Massengill, L.W.; Dodd, P.E.; Shaneyfelt, M.R.; Felix, J.A.; Schwank, J.R.; Haddad, N.F.; Lawrence, R.K.; Bowman, J.H.; Conde, R., “Impact of Ion Energy and Species on Single Event Effects Analysis,” IEEE Transactions on Nuclear Science, 55(4), December 2007, pp 2312-2321 Swift, G.M.; Guertin, S.M., “In-flight observations of multiple-bit upset in DRAMs,” IEEE Transactions on Nuclear Science 47 (6) December 2000, pp 2386-2391 Harboe-Sorensen, R.; Daly, E.; Teston, F.; Schweitzer, H.; Nartallo, R.; Perol, P.; Vandenbussche, F.; Dzitko, H.; Cretolle, J., “Observation and analysis of single event effects on-board the SOHO satellite,” IEEE Transactions on Nuclear Science, 49(3), June 2002, pp 1345- 1350 Goka, T.; Matsumoto, H.; Nemoto, N, “SEE flight data from Japanese satellites,” IEEE Transactions on Nuclear Science 45(6) December 1998 , pp 2771-2778

VIII. APPENDIX: MICROELECTRONIC DEVICE PARAMETERS AND FIG. 1 DATA TABLE I DEVICE PARAMETERS Device (ref)

FLUKA RCC / ISO/Cosine law

CRÈME-96 RPP x,y,z (um)

Onset (MeVcm2/mg)

Width (MeVcm2/mg)

Exponent

Limiting XS (um2)

Cosine Law

39.5, 39.5, 5.92

2.75

140

0.95

1560

ISS TMS44400 1Mx4 DRAM (25-27)

RCC (T/W 1)

5.48, 5.48, 5.48

0.99

7.7

1.3

30

ISS SMJ416400 4Mx4 DRAM (25-27)

RCC (T/W 2)

1.05, 1.05, 2.1

0.42

0.8

1.7

1.1

ISS KM44S32030T 128Mbit SDRAM (25-27)

RCC (T/W 0.1)

2.42, 2.42, 0.24

13

30

1

5.859

ISS KM44S32030T 128Mbit SDRAM (25-27)

RCC (T/W 0.1)

1.25, 1.25, 0.125

14

30

1

1.563

ISS KM44S32030T 128Mbit SDRAM (25-27)

RCC (T/W 0.1)

0.43, 0.43, 0.043

1.95

30

1.9

0.186

V4 XQR4VFX60 – BRAM (28)

RCC (T/W 2)

1.87, 1.87, 3.74

0.2

70

0.724

3.5

V4 XQR4VFX60 – Config. Memory (28)

RCC (T/W 2)

5.1, 5.1, 10.2

0.5

400

0.985

26

V5 LX330T – Config. Memory (28)

RCC (T/W 2)

3.36, 3.36, 6.72

0.5

30

1.5

11.3

Thuraya DSP Megagate ASIC (30)

ISO

2.5, 2.5, 1.76

2.7

20.6

1.2

6.3

ISO & RCC (T/W 1)

2, 2, 2

0.3

60

6

4

ISO

6.32, 6.32, 6.32

0.5

32

3

40

SOHO SMJ44100 4Mx1 (33)

RCC (T/W 0.25)

7.07, 7.07, 2

0.7

15

2.7

50

SOHO MHS CP65656EV 32kx8 SRAM (33)

RCC (T/W 0.25)

7.75, 7.75, 2

1.9

17

1.2

60

ETS-V PD4464D-20 64k SRAM (34)

RCC (T/W 0.05)

19, 19, 10

0.5

15

2.9

375

IMS1601EPI (29)

Mercury Messenger ASIC (31) Cassini OKI Solid State Recorder (32)

TABLE II IN-FLIGHT DATA AND FLUKA, CREME-96, AND FOM ESTIMATES Device

Median Shielding Mass g/cm2

CREME-96 Predicted SEU/bit day (CRÈME)

FOM Predicted SEU/bit day (FOM)

8.9 x 10 -8

1.1 x 10 -7

2.5 x 10 -7

7.2 x 10 -8

3.1 x 10 -8

6.8 x 10 -8

3.2 x 10 -9

5.1 x 10 -8

7.2 x 10 -8

9.6 x 10 -9

10 -9

10 -8

10 -8

2.1 x 10 -9

Spacecraft

Flight Env.

Ref.

In-Flight SEU/bit day (X)

ISS

ISS

25-27

TMS44400

10

8.5 x 10 -8

ISS

ISS

25-27

TMS44400

40

7.0 x 10 -8

ISS

ISS

25-27

SMJ416400

10

FLUKA Predicted SEU/bit day (FLUKA)

ISS

ISS

25-27

SMJ416400

40

3.7 x

ISS

ISS

25-27

KM44S32030T-GL

40

3.3 x 10 -10

2.8 x

2.2 x 10 -10

1.9 x 10 -10

2.0 x

2.0 x 10 -10

ISS MISSE-7

ISS

28

V4 XQR4VFX60 BRAM

0.8

4.2 x 10 -8

8.1 x 10 -8

8.6 x 10 -8

6.7 x 10 -9

ISS MISSE-7

ISS

28

V4 XQR4VFX60 – Config. Memory

0.8

3.8 x 10 -9

7.1 x 10 -9

9.1 x 10 -9

6.2 x 10 -10

ISS MISSE-7

ISS

28

V5 LX330T – Config. Memory

0.8

7.6 x 10 -9

6.4 x 10 -9

7 x 10 -9

1.9 x 10 -8

Space Shuttle

ISS

29

IMS1601EPI

34

3.1 x 10 -7

2.5 x 10 -7

2.7 x 10 -7

7.4 x 10 -8

Thuraya

GEO

30

ASIC 0.25 µ SRAM, IBM SA-12

0.7

5.3 x 10 -8

5.3 x 10 -8

7.9 x 10 -8

2.2 x 10 -7

Mercury Messenger

IP

31

ASIC “rad/SEE hard” SRAM

1.0

8.6 x 10 -10

5.8 x 10 -11 (1µ W)

2.9 x 10 -11

4.0 x 10 -9

Mercury Messenger

IP

31

ASIC “rad/SEE hard” SRAM

1.0

8.6 x 10 -10

9.3 x 10 -12 (no W)

2.9 x 10 -11

4.0 x 10 -9

Ca ssini

IP

32

OKI (4Mx1)

3.4

5.8 x 10 -8

2.5 x 10 -8

2.1 x 10 -8

1.9 x 10 -7

SOHO

IP

33

SMJ44100

1.0

5.9 x 10 -7

6.4 x 10 -7

1.2 x 10 -6

1.6 x 10 -6

1.0

1.7 x

10 -7

10 -6

2.5 x

10 -6

3.1 x 10 -6

1.7 x

10 -6

9.3 x

10 -6

1.24 x 10 -5

SOHO ETS-V

IP GEO

33 34

CP65656EV PD4464D-20

5.8

1.6 x 6x

10 -6