Correlation of Prediction to On-Orbit SEU Performance for a ...

IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 54, NO. 6, DECEMBER 2007

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Correlation of Prediction to On-Orbit SEU Performance for a Commercial 0.25-m CMOS SRAM D. L. Hansen, K. Jobe, J. Whittington, M. Shoga, and D. A. Sunderland

Abstract—Boeing Satellite Development Center (SDC) geomoblie satellites have experienced a number of very large solar flares during their operational life. Comparison performance in a geostationary orbit to predictions based on heavy-ion ground testing for a digital signal processor (DSP) payload based on 0.25- m CMOS, megagate-ASICs will be presented. Performance during flare peaks will be shown, and comparisons will be made between the response measured during each event, and some commonly used models. The results show that the careful technology selection and SEU mitigation techniques have enabled the spacecraft to operate through several periods of high radiation flux with no ill effects to the system. Index Terms—Radiation, single event effects, solar particle events, space.

I. INTRODUCTION HE space community faces a number of challenges as technologies evolve. The need for robust technologies capable of operating in harsh radiation environments experienced by space hardware is well known [1] and often in conflict with the need for performance that that is frequently required if system capabilities are to match customer demands. These new technologies can provide opportunities for both great success and for spectacular failure. The latter is especially true if new features within the devices are not well understood, or if the optimization of design tools traps the unwary. This report describes a design success story, and compares the on-orbit performance of a system that relies heavily on a commercial, world-class technology. Several geomobile communications systems have been placed into orbit [2]. We designate the two satellites studied here as G1 which began commercial operations in 2001, and G2 which began operation in 2003. Both employ an on-board, 2.5 kW digital signal processor with a throughput that is approximately equivalent to 3000 Pentium processors (roughly 14 trillion operations per second). The DSPs were fabricated using the standard process and library, however, in order to do this, special care was taken in understanding the radiation vulnerability of the parts. This allowed the architecture and megagate ASIC designs to be specially crafted to handle the rigors of space operation. In particular, great care was taken in both the technology selection, and the design, to mitigate single-event effects. The upsets analyzed in this paper were all detected and

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Manuscript received May 3, 2007; revised September 18, 2007. The authors are with the Boeing Satellite Development Center, Los Angeles, CA 90009-2919 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TNS.2007.908787

corrected by error correction techniques in the DSP. Thus the errors analyzed here had no effect on system operation and on-orbit performance shows that the careful parts selection and system SEU mitigation techniques were successful in enabling both satellites to achieve cutting edge DSP processing with high operational stability during significant periods of hostile space weather. II. TECHNOLOGY CHARACTERIZATION The DSP was built from 2.5V, 0.25 m (polysilicon gate length) ASICs fabricated from IBMs SA-12 library. SEU tests were performed on the Thunder test chip, which is the library validation test vehicle used by IBM to qualify many of the SA-12 ASIC family library elements. This chip consists of 32 bit, many test structures; the tested structure is a 4k single-port SRAM. These devices are representative of IBM’s advanced commercial ASIC technologies at the time; and were not specifically designed to be radiation hard. The Thunder test chip is ‘flip chip’ mounted in a land grid array (LGA) package. In order for particles to reach the active region of the device, the ion beam has to be applied from the bottom of the chip (the back side). Since the substrate is thick (several hundred microns), the chips were thinned down to approximately 30 to 100 m to enable the test ions to penetrate and reach the active region of the device. Note that galactic cosmic rays encountered in the space environment are more energetic than these test ions and can penetrate the LGA without considerable attenuation. Tests were performed in December of 1997 at the Texas A&M University Cyclotron using four high-energy ions (25 MeV/amu); Ne, Ar, Kr, and Xe. Degrader foils were used to adjust the linear energy transfer (LET) of each ion. The penetration range of the test ions was calculated to make sure an adequate ion path length existed for depositing energy at the critical depths within the tested devices. The ions used, their adjusted LET, and the remaining penetration ranges of the “degraded” ions are shown in Table I. The SRAM was tested in two modes: all 0’s and all 1’s. The test cycle consisted of a write for all addresses, followed by a read for all addresses, looping back to the write until a total of errors were recorded. The cross section for both the all 0’s and all 1’s patterns are shown in Fig. 1. The data indicates that the cross section is approximately the same for both patterns. The error count as a function of the address location was found to be approximately constant. During testing, the write and read cycles take the same amount of time. As a result, errors occurring in each memory location would not be detected

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method was initially introduced before the wide distribution of the more exact codes and is known to give conservative, overestimates of upset rate at GEO orbit. The refinement of the calculation method for solar minimum calculates the rates using the formula

TABLE I CHARACTERISTICS OF TEST IONS USED IN SEU TESTS

(2) with in cm MeV cm mg is the LET at 25% of and has units of MeV cm mg . Using this method, upset bit day . we calculate an upset rate of The second method utilizes the effective-flux model and follows the explanation found in [4]. The model assumes a thin device with an isotropic flux. The method transforms the environmental flux to an effective flux for a specific cutoff angle. The implementation in [4] then calculates the rate as (3)

Fig. 1. 0.25 m SRAM Cross-Section Characteristics. The line represents a fit to the data taken with both the all ”0” pattern and the all “1” pattern.

during about half the exposure time. That is to say that errors occurring during the time between a read of a given address and subsequent write to the same address would not be detected. Thus we must multiply the measured cross section by two. This correction has been included in the data in Fig. 1. III. PREDICTION OF ON-ORBIT PERFORMANCE The first step in SEU rate estimation is fitting the data to an appropriate model. In these tests we used a Weibull curve with the form (1) is represented by the solid line in Fig. 1. The saturated cross-section value of 6.3 m /bit was determined by calculating the average cross section corrected for the read-write dead time of the data points having linear energy transfer (LET) values in the range 50–60 MeV cm mg . The threshold LET ( MeV cm mg )) was estimated based on the data. Width and power curve parameters were then determined by a linear curve fit. For comparison purposes we will calculate the upset rate using a number of different methods. The first method for rate calculation is the Petersen figure of merit (FOM) [3]. This

For our case, this gives an upset rate of upset bit day CREME96 is an update of the Cosmic Ray Effects on Micro-Electronics code, a widely-used suite of programs for creating numerical models of the ionizing-radiation environment in near-Earth orbits and for evaluating radiation effects on spacecraft [5]. The upset rates for galactic cosmic ray background at solar minimum and maximum conditions as well as for worst week, worst day, and worst 5 minute conditions were calculated using CREME96. An environmental model was generated for the GEO orbit and with 100 mils of shielding. The RPP used for the calculations had the lateral dimensions m. Selecting an RPP thickness that conforms to the directional dependence of the device cross section is not trivial and considerable effort has been devoted to addressing this issue [6]–[8]. For simplicity we chose the m for modeling in CREME96 device depth to be [9]. In our example this produces an RPP that is slightly wider than it is tall. The implied directional dependence is believed to be credible, even though this has not been verified with terrestrial experiments. Using these values, we calculate an upset bit day for solar minimum upset rate of upset bit day for solar maximum. We also and note that using a depth of 20% of the lateral dimensions [8] gives a result similar to the Petersen FOM, while assuming a depth of 1% of the lateral dimensions gives a result similar to the Binder model. However none of the rules mentioned here for determining the device depth have universal applicability. IV. ON-ORBIT PERFORMANCE There are several digital telemetry registers providing SEU data on the satellites. These registers serve as cumulative counters and are incremented when upsets seen in the static RAM of the DSP are corrected by the error correction code [2]. Thus it is important to note that the errors detected and analyzed in this paper had no affect on satellite operation. The register values are updated at varying intervals and transmitted as part of the telemetry data. The data from these registers were collected from G1 over the period Jan, 2001 to April 2003, and

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TABLE II UPSET RATES FOR FLARE EVENTS

Fig. 2. Monthly average upset rate for G1 and G2. The open symbols represent the months during which a CME occurred (Table II), the symbols with an “ ” indicate months preceding and following a CME. The solid line represents a fit to the data excluding the months during which a CME was recorded. The broken lines represent rates calculated using the different methods.

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from G2 from September, 2003 to December 2005. The data consists of a time stamp and the register reading representing the cumulative number of SEUs recorded by each register over the course of the mission. Typically, the register count incre% the counter was seen mented by 1. In a small number to increment by 2; this is most likely the result of two successive upsets occuring between register readout intervals. There are 22 Mbits of SRAM thus upsets to readout registers monitoring the registers occur infrequently relative to the number of upsets seen in the SRAM. No effort was made to correct for register upsets. In addition, temporary increases followed by an equivalent decrease in the register counts were obsevered periodically. Since the registers have no mechanism for decrementing, and the register values return to the number seen before the jump in counts, these temporary increases were attributed to glitches in the telemetry and are ignored in this report. Fig. 2 shows the upset rate in upsets bit day for the G1 and G2 spacecraft. The rates in Fig. 2 are calculated as the number of upsets/bit recorded in a calendar month divided by the number of days in that month. In order to provide a convenient estimate of the background rates, a linear fit was applied to the data, however the months during which the four largest CME events were recorded during this time period (Table II and open symbols in Fig. 2) were excluded from the fit. The resulting line had the equation (4) For this fit, a sequential serial number was used for the date. In this case January 1, 1900 is serial number 1, and January 1, 2008 is serial number 39448 (39 448 days after January 1, 1900). While the solar activity may not truly follow a linear model during the period that this data covers (January 2001 to February 2006), the sunspot number is steadily decreasing (Fig. 3) and the month to month variation in upset rate due to the environment is about a factor of 2 (Fig. 2). Thus a linear fit provides a reasonable approximation of the background upset rate. Other

TABLE III BACKGROUND RATES

TABLE IV UPSET RATES (UPSETS BIT

DAY

)

entries in Table II include the upset rate for the worst 5-minute, worst day, and worst week periods. These were found by determining the 5-minute, day, and week period when the most upsets were recorded, and then normalilzing this to the number of bits and appropriate amount of time. To provide an upset rate “envelope” we also performed a fit to the points with the highest and lowest upset rates. The results are summarized in Table III. From the data in Tables II–III, we can see that the environment encountered by the devices behind the spacecraft shielding is milder than the CREME96 model behind 100 mils for solar min and CME conditions. In Fig. 2 the calculated upset rates are plotted with the on orbit data. The CREME 96 rates are the most accurate of the methods used. The solar minimum rate is about 25% greater than the measured rate. The solar maximum rate calculated with m is about 30% of the upset rate measured, however,

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Fig. 3. Sunspot number versus date for solar cycle number 23. (Courtesy of William Murtaugh, NOAA, http://www.sec.noaa.gov/SolarCycle/index.html).

we note that the calculated rate is lower than the measured rate, in real applications this could prove problematic. The Petersen FOM and Binder rates are about 4–5 times greater than the rates for solar minimum extrapolated from the on orbit data. The upset rates are summarized in Table IV. Additionally, we observe that upset rates during the months immediately preceding and following a CME (annotated with an “ ” in Fig. 2) are consistently among the lowest recorded. Comparing this data to the NOAA data (Fig. 3) from solar cycle 23 [10] we see that as is expected [11], the background rate in Fig. 2 is increasing as the solar cycle approaches solar minimum. The peak period for the sunspot number corresponding to solar maximum runs from about January 2000 to January 2002 (Fig. 3). The G1 data covers part of this period (May 2001 to January 2002) during which the average upset upset bit day . Using the linear rate measured is fit from (1) we calculate the upset rate during solar minimum upsets bit day based on and obtain a value of the fit for January 2007. This gives a value of 1.5 for the ratio of Rate[solar min]/Rate[solar max]. This is significantly lower than the value of 7.4 calculated using CREME96 (Table III). For comparison purposes, on-orbit upset rates measured in SRAM during solar cycle 22 showed a ratio of Rate[solar min]/Rate[solar max] between 1.06 and 2.2 [12]. Comparing MeV, [13] gives a ratio our result to the flux of protons ; based on our fit we get a ratio of 1.3 for the upset rates for these two periods. Caution must be used in drawing any conclusions from these ratios. The amount of shielding on the

spacecraft is most likely greater than the 100 mils of Al used in the CREME96 model. In Figs. 4–7, the upset rates for the satellites are shown along with the proton and heavy ion fluxes during the four largest CME events recorded during this time period. Since the upsets to the satellites typically come in 1 at a time, the upset rate in these plots is calculated as the inverse of amount of time between upsets. In other words, an “instantaneous upset rate” is calculated by dividing a single observed upset by the amount of time since the previous upset, and normalizing this to the number of bits. The proton fluxes used in Figs. 4–7 were taken from data collected by the energetic particle sensor (EPS) on board the Geostationary Operational Environmental Satellites (GOES). Data from these satellites can be found at [14], and details of the instruments can be found in [15]. In Figs. 4–7, the proton flux in three energy ranges are plotted (15–40 MeV, 80–165 MeV and 165–500 MeV). Proton-flux data from GOES-10 was used used for comparison to the G1 upset rates, while the GOES-11 satellite provided data for comparison to the G2 upset rates. It is expected that the effects of the lower energy protons will be ameliorated by the presence of shielding within the spacecraft, the flux of the lower energy protons is included to verify that this is the case. Data for the heavy-ion flux is provided by the solar isotope spectrometer (SIS) on board the Advanced Composition Explorer (ACE) [16]. ACE orbits the L1 Sun-Earth Lagrangian point. Archived data can be found at [17]. The spectrometer resolves the ion energies into for 8 different energy ranges that

HANSEN et al.: CORRELATION OF PREDICTION TO ON-ORBIT SEU PERFORMANCE

Fig. 4. Upset rate for G1 during the April 15, 2001 CME event. Proton flux measured by GOES-10 and Fe flux from ACE-SIS are also shown.

Fig. 5. Upset rate for G1 during the November 5, 2001 CME event. Proton flux measured by GOES-10 and Fe flux from ACE-SIS are also shown.

are independent for each nucleus. The ACE-SIS data shown in Figs. 4–7 is the flux of Fe ions with energies between 70–117

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Fig. 6. Upset rate for G2 during the October 28, 2003 CME event. Proton flux measured by GOES-11 and Fe flux from ACE-SIS are also shown.

Fig. 7. Upset rate for G2 during the January 20, 2005 CME event. Proton flux measured by GOES-11 and Fe flux from ACE-SIS are also shown.

MeV/nucleon. This was chosen because the data has a reasonable flux and the ions represented are likely to be capable of initiating upsets. The ACE-SIS data provides an excellent means

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TABLE V PROTON AND ION FLUENCES [10]

of determining when ions arrive, and the temporal characteristics of the flux. It should be noted that the ACE-SIS data is collected in 256 s time bins and in Figs. 4–7 only those bins where a non-zero flux was measured are shown. Previous studies [18] have mentioned the difficulties in correlating ACE-SIS to upset rates. This arises for a number of reasons including the fact that ACE-SIS does not record the highest LET ions. Thus for further comparison, we also show data collected from the CREDO experiment on the microelectronics and photonics testbed (MPTB) experiment [19]–[21] in Table V, [13]. The MPTB mission is in a high-inclination elliptical orbit a large part of which extends beyond the earth’s magnetic field to an altitude of 39 200 km at apogee [19]. The data presented in [13] is from the Cosmic Radiation Dosimetry experiment (CREDO). The proton telescope MeV, while in CREDO measures the proton flux for energy the ion monitors measures the LET over the range of 0.1 to 20 MeV cm mg in 16 channels with the upper channel giving an integral measurement above the higher level [20]. It is difficult to estimate the shielded ion flux from the data in [13] however we note that the events with the highest ion flux show the highest upset rates. This same correlation is not true for the proton fluxes. It is important to note that both sources of the heavy-ion flux data come from satellites in orbits different from the G1 and G2 satellites where the errors are being detected. As mentioned previously the G1, G2 and GOES satellites are in geostationary orbits, while MPTB is in an eliptical orbit and ACE is at the L1 Lagrangian point. In addition, even when comparing flares that originate in the same solar active region, the ratio of the fluxes of the different elements as well as the energy spectra can vary significantly [23]. Consequently care should be taken when using the flux data from these sources to determine the relative importance of protons and heavy ions in causing upsets. During the time period over which the data was collected, four periods with an increased number of SEUs in the DSP were measured (April 2001, November 2001, October 2003, and January 2005). These are shown in more detail in Figs. 4–7. While each event will be discussed in greater depth later, a few general comments are consistent among all of the events. In comparing the proton flux with the SEU rate we see that an increase in

the SEU rate is accompanied by an increase in the proton flux, however, the flux for even the high energy protons and the accompanying increase in the SEU rate do not match in terms of duration (Figs. 4–7). In addition, during a number of instances an increase in the proton flux is not accompanied by an increase in SEU rate. The heavy-ion flux is typically a better match to the upset rate in terms of peak duration, however, increases in heavy-ion flux do not always cause a corresponding increase in the upset rate, as is seen on Oct. 28, 2003 (Fig. 6). In addition, the magnitude of change in heavy ion flux is not necessarily proportional to the change in upset rate, as is seen in the April 3 and April 15, 2001 peaks (Fig. 4). These discrepancies show that the upset rate is a complex function not only of the proton and heavy-ion flux, but also of the energy spectra of the particles. The April 2001 event or Easter Storm (Fig. 4) showed elevated proton fluxes between approximately March 29, and April 23, 2001. The high-LET, heavy-ion component was comparable to CREME 96 worst day, however the proton flux was less severe [20]. Of the events discussed in [13] the April 2001 event shows the highest heavy ion flux. The peak proton and heavy ion fluxes occurred on April 15 and corresponded to the peak upset rate for the G1 satellite. An additional period of increased heavy-ion flux was observed on April 2. While the peak Fe flux on April 2 was about half that measured on April 15, the upset rate for G1 was more than an order of magnitude lower (Fig. 4). The Nov. 5, 2001 or Guy Fawkes Day Event (Fig. 5) exceeds CREME 96 worst case spectrum at low LET [20]. The GOES proton flux shows a series of peaks between Oct. 22 to Dec. 2, the primary peaks correspond to the two main periods of enhanced heavy-ion flux from Nov. 4 to Nov. 11 and Nov. 22 to Nov. 28 seen in the ACE data. It is interesting to note that the GOES-10 data shows a drop in flux between Nov. 5 and 6 of less than an order of magnitude. Over the same period the G1 upset rate drops by more than three orders of magnitude, and the ACE-SIS heavy-ion flux increases. The “dips” seen by GOES-10 and G1 satellite (geo orbit) satellites are likely a result of the fact that the increased flux of solar particles lasted sufficiently long for both of these satellites to experience an eclipse by the earth. Because of its orbit about the Sun-Earth Lagrangian million km toward the Sun along the line connecting point the Sun and Earth, the ACE satellite does not experience eclipses by the earth. We note that since the rotational period of the sun is about 25 days, it is unlikely that the dip and subsequent increase in flux are the result of directional changes in the flare. This fact points out an added complexity in interpreting the data in that not only are the upset rates a function of the flux and spectrum of solar particles but also of the position of the satellites. The October 2003 or Halloween Storms (Fig. 6) were associated with an X17 flare on Oct. 28 and the largest X-ray flare ever recorded (X28) on Nov. 4. It should be noted that the CME associated with this flare was not directly aligned toward the earth, and thus the effects were not as severe as they could have been. Like the November 2001 event, the proton flux exceeded the CREME96 worst day flux in the low LET regime [13]. The flux for the Halloween events showed five distinct peaks in heavy-ion and proton flux on Oct. 27, 28, 29, Nov. 2 and Nov. 5. Of these the highest upset rate was produced by the

HANSEN et al.: CORRELATION OF PREDICTION TO ON-ORBIT SEU PERFORMANCE

Fig. 8. Cumulative upsets and duration of each CME event.

Oct. 29 event; while the event on Oct. 27 did not result in a corresponding increase in the upset rate. The January 2005 event or Martin Luther King Jr. day storm was associated with an X7 flare and, with a proton flux of 652 sr cm ) for MeV protons and pfu (particles s 1860 pfu for MeV protons, it was the most highly energetic proton event of solar cycle 23 [24]. This is demonstrated in the fact that of the events shown in Figs. 4–7, the GOES-11 data for the Jan. 2005 event has the highest flux of protons with energies greater than 80 MeV. We also note that because it is more recent, the depth of analysis in the published literature on the January 2005 event is much less than that available for the other events described here. To provide another means of comparing the events, in Fig. 8 we present the cumulative number of counts as a function of denotes the beginning time. On the horizontal axis, of the highest upset rate portion of the event, and the values on the abscissa are given as the number of days before (negative numbers) or after (positive numbers) this. On the vertical axis, the number of cumulative counts has been shifted so that the event begins with 0 counts. That is not to say that there were no upsets prior to this, but instead we are plotting the cumulative number of upsets during the event. Of the events recorded here, the October 2003 and January 2005 events were responsible for the most upsets. However, it is interesting to note that the curves for the January 2005 and April 2001 events (Fig. 8) have the largest slopes and thus the highest upset rates. This is emphasized in Fig. 9 where the data from Fig. 8 is shown with the horizontal axis expanded. In Fig. 9, it is clear that in the January 2005 event most of the upsets occurred in the first day, while for the October 2003 event upset pattern is spread out over more than one day. The higher upset rates in the January 2005 and April 2001 events correlate to the highest 165–500 MeV proton fluxes measured during the CMEs shown in Figs. 4–7. It seems likely that the relatively large flux of high energy protons at the start of the CME causes the high initial upset rates observed. The lower upset rate for the October 2003 event may be in part the result of the fact that the CME associated with this

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Fig. 9. Data from Fig. 8 plotted with the horizontal axis expanded. In all four events, the worst upset rate portion of the flare subsides after about 1.5 days.

Fig. 10. Flare multiplication factors for the worst 5 minute, worst day, and worst week upset rates. Multiplication factors were calculated as the ratio of the upset rate for the specified interval to the background upset rate (Table II). The dashed lines represent the flare multiplication factors calculated using CREME 96, and are calculated as the ratio of the upset rate during the interval listed to the upset rate during solar minimum. CREME 96 appears to over-estimate the impact of flares on upset rates.

event was not directly aligned toward earth. In any case, for all four flares observed here, the highest dose rate portion of the event subsides after about 1.5 days (Fig. 9). This point can be shown further by the calculation of the flare multiplication factors (FMF) shown in Fig. 10. These are determined by dividing the upset rates for the worst 5 minutes, the worst day and the worst week of the flare by the background upset rate. The FMF values are given in Table VI, while numerical data used to calculate the FMF is shown in Table II, where the background rates given are calculated from the linear fit described in (1). For comparison previous studies using data on SRAM in orbit from 1988 to 1997 [12] measured a worst week FMF of 67 and worst day FMF of 241. Both of these numbers were calculated relative to solar maximum, whereas the numbers we gave are calculated relative to the background rate. At most we would expect this to cause the measured FMF to differ

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TABLE VI FLARE MULTIPLICATION FACTORS

by a factor of 2; instead our FMF differ from those measured in [12] by about an order of magnitude. This most likely underscores the differences in the two technologies used. While in were similar, the value for both cases the values for in [12] was much higher. The FMF calculated using CREME 96 (Table VI) are plotted in Fig. 10 as the dashed lines. For the flare events analyzed here, the worst week FMF calculated using CREME 96 is almost 2 orders of magnitude higher than those measured, the CREME 96 worst day FMF is about a 40 times the measured value, and the CREME 96 worst 5 minutes FMF is larger than the measured value by a factor of 2–3. This is not surprising since the CREME 96 environment is designed to be worst case, and the fact that because of the additional electronics and structural supports, the effective amount of shielding for the DSP in the satellites is likely greater than the 100 mils used in the CREME 96 calcuations. Of the events listed here, the January 2005 event has the largest FMF for the worst 5-minutes with the April 2001 event being second. This correlates well with the large upset rate and number of errors seen in Figs. 8–9. Previous studies [19] saw increases in DRAM upset rate by factors of 30–50 for the Bastille Day event associated with an increase in proton flux by a factor of 104, and pointed to this disparity as evidence of the importance of the different upset mechanisms for heavy-ion and proton upsets. The April 2001 event has the lowest proton fluence, but a higher fluence in the high LET part of the spectrum than the other flares analyzed in [13]. Further analysis is needed to determine if the higher worst 5 minutes FMF in the January 2005 event is the result of an increased heavy-ion flux or the result of the increased high energy proton flux known to be associated with the January 2005 event. V. CONCLUSION The G1 and G2 satellites have shown continuous operation through each of the flare events discussed here, providing evidence that the technology selection and implementation of SEU mitigation techniques in the DSP on board has been successful in detecting and correcting upsets caused by energetic particles. We have also shown that the upset rate is a complex function of the proton and heavy-ion spectrum. In spite of this, using a few modest assumptions it is possible to calculate the background upset rate at solar minimum (the worst case background) for the satellite with reasonable accuracy using CREME 96. In addition

the FMFs measured on orbit are consistently lower than those calculated using CREME 96. This is likely due to additional shielding on board the spacecraft. However the rates calculated m under-estimated using the solar maximum model with the measured upset rate. It should be noted that other choices for z could have been considered. Although not shown in the tables, times the lateral dimensions the particular choice of will produce good agreement between calculated and observed background rate during solar maximum, but calculated rates will exceed observed rates during solar minimum. No choice for z was found that would produce agreement between calculated and observed rates during both time periods. We also found that the models by Petersen and Binder provided a conservative calculation of the upset rates and for the period studied here, provided an upper bound on the upset rates during background periods as well as during flare activity. ACKNOWLEDGMENT The authors would like to thank W. Murtaugh, NOAA, for the use of his sunspot data; R. Fountain and J. Haskell for their help in tracking the data, M. Bodeau for his help in reviewing the manuscript; and M. Bustamante, R.D. Jobe, E. Limberg, and B. Paine for their continued support. 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] D. A. Sunderland, G. L. Duncan, B. J. Rasmussen, H. E. Nichols, D. T. Kain, L. C. Lee, B. A. Clebowicz, and R. W. Hollis IV, “Megagate ASICs for the Thuraya satellite digital signal processor,” in Proc. Int. Symp. Quality Electronic Design (ISQED’02), 2002, pp. 479–486. [3] E. L. Petersen, J. C. Pickel, E. C. Smith, P. J. Rudeck, and J. R. Letaw, “Geometrical factors in SEE rate calculations,” IEEE Trans. Nucl. Sci., vol. 40, no. 6, pp. 1888–1908, Dec. 1993. [4] D. Binder, “Analytic SEU rate calculation compared to space data,” IEEE Trans. Nucl. Sci., vol. 35, no. 6, pp. 1570–1572, Dec. 1988. [5] A. J. Tylka et al., “CREME96: A revision of the cosmic ray effects on Micro-electronics code,” IEEE Trans. Nucl. Sci. vol. 44, no. 6, pp. 2150–2160, Dec. 1997. [6] L. W. Connell, F. W. Sexton, and A. K. Prinja, “Further development of the Heavy Ion Cross section for single eventUPset: Model (HICUP),” IEEE Trans. Nucl. Sci., vol. 42, no. 6, pp. 2026–2034, Dec. 1995. [7] L. D. Edmonds, “A method for correcting cosine-law errors in SEU test data,” IEEE Trans. Nucl. Sci., vol. 49, no. 3, pp. 1522–1538, June 2002. [8] L. D. Edmonds, C. E. Barnes, and L. Z. Scheick, “An introduction to space radiation effects on microelectronics,” Jet Propulsion Lab. Publication 00-06, May 2000 [Online]. Available: http://parts.jpl.nasa.gov/ [9] E. L. Petersen, J. C. Pickel, J. H. Adams, Jr., and E. C. Smith, “Rate prediction for single event effects—A critique,” IEEE Trans. Nucl. Sci., vol. 39, no. 6, pp. 1577–1598, Dec. 1992. [10] Courtesy of William Murtaugh, NOAA, [Online]. Available: http://www.sec.noaa.gov/SolarCycle/index.html [11] J. L. Barth, C. S. Dyer, and E. G. Stassinopoulos, “Space, atmospheric, and terrestrial radiation environments,” IEEE Trans. Nucl. Sci., vol. 50, no. 3, pp. 466–482, Jun. 2003. [12] T. Goka, H. Matsumoto, and N. Nemoto, “SEE flight data from Japanese satellites,” IEEE Trans. Nucl. Sci., vol. 45, no. 6, pp. 2771–2778, Dec. 1998. [13] C. S. Dyer, K. Hunter, S. Clucas, and A. Campbell, “Observation of the solar particle events of October and Nov. 2003 from CREDO and MPTB,” IEEE Trans. Nucl. Sci., vol. 51, no. 6, pp. 3388–3393, Dec. 2004. [14] Data From the GOES Satellites, National Weather Service Space Environment Center. [Online]. Available: http://www.sec.noaa.gov/ [Online]. Available: http://spidr.ngdc.noaa.gov/spidr/index.jsp (archived data)

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