# Measurement of Single Event Effect cross section induced by monoenergetic
protons on a $130$ nm ASIC

D. Boumediene, F. Jouve, D. Lambert, R. Madar, S. Manen, O. Perrin, L. Royer,
A. Soulier, R. Vandaele

###### Abstract

Designing integrated circuits in radiation environments such as the High
Luminosity LHC (HL-LHC) is challenging. Integrated circuits will be exposed to
radiation-induced Single Event Effects (SEE). In deep sub-micron technology
devices, the impact of SEEs can be mitigated by triple modular redundancy. The
triplication of the most sensitive data is used to recover most of the data
corruption induced by interacting particles. One type of SEE, called single
event upset (SEU), is studied in this paper. The SEU cross-section and the
performance of the triplication are estimated using an ASIC prototype exposed
to a beam of protons. The SEU cross-section is measured and systematic
difference between $1\rightarrow{0}$ and $0\rightarrow{1}$ bit flip rates is
observed. The efficiency of the mitigation method is investigated.

##  1 Introduction

Designing integrated circuits in radiation environments such as the High
Luminosity LHC (HL-LHC) is challenging. Integrated circuits will be exposed to
radiation-induced Single Event Effects (SEE), especially Single Event Upsets
(SEU). In deep sub-micron technology devices, the impact of SEEs can be
mitigated by triple modular redundancy [1, 2]. The triplication of the most
sensitive data is used to recover the data corruption induced by interacting
particles.

SEU can be studied with different types of ionizing particles [3]. Heavy Ions
can be used, offering the advantage of testing an extensive range of Linear
Deposit Energies (LETs). However, a model has to be used to extrapolate the
measurements to the final environment. Protons of at least $20~{}$ can be used
to estimate the SEU effect, especially for the HL-LHC environment where the
detector components are shielded against low-energy particles and where the
expected fluences are usually expressed in unit number of $1\,$ neutrons
equivalent per squared centimeter,
$\mathrm{n}_{\rm{eq}}^{1\,}\mathrm{c}{}^{-2}$ unit. The energy dependence can
be taken into account to refine the predictions.

In this paper, the SEU cross-section and the performance of the triplication
are estimated using an ASIC prototype exposed to a monoenergetic beam of
protons. This tested ASIC mainly contains a static random-access memory (SRAM)
system made of flip-flops, capable of handling a latency of $38.4\,\mu$s,
required by the trigger system of the experiment. This SRAM is naturally
sensitive to SEU, mitigated by auto-correction using triplication.

This paper aims to determine the bit flip rate on a 130 nm technology. The
corresponding SEU cross-section $\sigma_{\rm{SEU}}$ was measured in [4] and is
expected to be in the order of $\sigma_{\rm{SEU}}=(6\pm
1)\,10^{-14}\mathrm{c}{}^{2}$. This rate, combined with a triplication design,
leads to a residual autocorrection failure rate. This failure rate is
estimated for an 1600 bits ASIC, and it has to be negligible during an average
data-taking period of 1 day.

Section 2 describes the facility and the setup used to collect the data. The
data are analyzed in Section 3, and three results are presented:

  * •

the SEU cross-section for single bits,

  * •

an estimate of the triplication failure rate using as a study case a 1600
triplicated bit ASIC at the HL-LHC, and

  * •

the direct measurement of triplication failures.

##  2 Experimental setup

###  2.1 The MEDICYC facility and beam monitoring

The SEU cross-section was measured during a test beam campaign at the Centre
Antoine Lacassagne (CAL) in 2021, where the MEDICYC cyclotron [5, 6] provides
proton beams. MEDICYC is equipped with several proton beamlines, two of which
are in operation at the moment. A clinical beamline provides proton therapy of
ocular melanoma. A research beamline has recently been equipped and is
proposed to researchers and industry for R&D programs.

The subsequent measurements were performed using the low energy treatment
line, where a $62.0\,$ proton beam was produced with up to $300\,\mathrm{n}{}$
intensity. This energy corresponds to a LET value of
$8.7\,\,\mathrm{c}{}^{2}/\mathrm{m}{}$ in silicon dioxide. The beamline
calibration and monitoring control the intensity at the 3 per mil level at the
highest intensity.

###  2.2 Integrated circuit and data taking

The $130\,\mathrm{n}{}$ TSMC ASIC prototype was designed to test the
performances of digital blocks when exposed to radiation. One of these tests
is dedicated to the SEE study. Sixty-four 8-bit registers were implemented and
accessible through an $\mathrm{I^{2}C}$ port. They are fully dedicated to SEU
counting, and their outputs have no other function in the chip. Each bit of a
register is triplicated, and automatic corrections are applied to every
triplicated-bits of the register at $40~{}\mathrm{M}{}$ using a majority voter
logic.

Figure 1: View of the ASIC where the flip-flops are highlighted by white
rectangles.  Figure 2: View of the ASIC (located under a black resin) on its
host board mounted on the beamline.

Each triplicated flip-flops are physically spaced by
$15~{}\mathrm{\SIUnitSymbolMicro}{}$ in the layout and shown in Fig. 1. The
implementation of the Register Transfer Level (RTL) is done with a 2008
version of the CERN TMRG tool [7]. It is integrated with the digital flow to
automatize the process of triplication. The TMRG toolset generates constraints
that ensure that triplicated items are not placed too close to each other
while allowing the designer to decide which blocks or signals will be
triplicated. The clock, reset, and control signals are not triplicated in the
ASIC prototype. The reset is synchronous with the $40~{}\mathrm{M}{}$ clock,
and each register can have a different reset value. If only one flip-flop has
its value changed due to SEU, the voted value is overwritten inside the faulty
flip-flop thanks to the auto-correction system. The voter also raises an error
flag if the three flip-flops do not have the same value. This error flag is
then used to increment counters to count SEU. Four counters are associated
with the correction mechanism. Each counter covers 16 registers, called
groups, and is incremented by one if a correction is applied to any one of
them. The four counter values are stored in triplicated registers. The total
number of bits monitored in the SEU rate measurement is 1536. The ASIC is
mounted on a custom board, shown in Fig. 2, that routes the power supplies,
the clock and various probe signals.

A microcontroller, linked to an $\mathrm{I^{2}C}$, is used to communicate with
the ASIC, read or reset the counters, and control the register contents.
External devices ensure a 40 MHz clock as well as a power supply. The various
counters were readout, and the registers reinitialized, after each reading,
with predefined words at a typical frequency of $100~{}$.

The ASIC was aligned with the proton beam covering the device surface with a
homogeneity better than 5%. Data were recorded using two beam intensities
during several data-taking periods, four at high intensity and one at low
intensity. The ASIC was exposed to a total fluence of
$8.7~{}10^{14}~{}\mathrm{p}\,{\mathrm{c}}^{-2}$.

##  3 SEU Cross-section measurement and auto-correction failure

Two methods are presented to measure the SEU cross-section. The first one
relies on the error rate measurement (section 3.1). The second method exploits
the randomness of the SEU, which must result in duration between two
consecutive errors $\Delta t$ distributed according to a decreasing
exponential (section 3.2).

Two methods are presented to estimate the auto-correction failure rate. The
first one is based on the probability of having two simultaneous SEUs in a
triplicated system. It is computed from the measured SEU cross-section
(section 3.3). The second method consists in counting the actual number of
events in which the auto-correction failed (section 3.4). For completeness, a
Monte Carlos simulation is performed and compared to data (section 3.5).

###  3.1 SEU cross-section

Figure 3:  Cumulative number of auto-corrections before (top) and after
(bottom) offline corrections from data corruption and overflow in the counter.

The registers were set to 0 during one period and to 1 during four periods.
The SEE rate estimate relies on the auto-correction counters. These SEE counts
are expected to be mainly induced by SEU. A few jumps in the counters were
observed and are interpreted as non-corrected SEEs on the counter itself.
These jumps are then filtered in order to keep only jumps related to SEU.
Also, when a counter reaches 255 it goes back to zero at the next
incrementation. These resets are also corrected. The number of errors before
and after treatment is shown in Fig. 3 as a function of time for one of the
periods and groups. The number of errors normalized to the beam intensity is
used to extract the SEU cross-section per bit, as summarized in Table 1. A
rate is computed for each period and group, and a global rate is obtained by
fitting these individual values assuming an error occurrence following a
Poisson distribution. The rate as a function of time, as well as the fit
result for the sixteen datasets at high intensity, are shown in Fig. 4. The
final rate value, and its uncertainty is derived from the mean and the mean
error over the different periods and groups. The average cross-section is
estimated to be

$\sigma_{\rm{SEU}^{1{\rightarrow}0}}=(7.90\pm
0.13)~{}10^{-14}\,\mathrm{c}{}^{2}$
$\sigma_{\rm{SEU}^{0{\rightarrow}1}}=(6.77\pm
0.41)~{}10^{-14}\,\mathrm{c}{}^{2}$

The systematic difference between $1\rightarrow{0}$ and $0\rightarrow{1}$ bit
flip rates is significant and already reported, eg. in ref. [4]. The
dependency to the LET, and therefore to the beam energy, as shown in ref. [3,
8], has to be considered for extrapolations at different energy scales.

Table 1:  SEE cross-sections, $\sigma_{\mathrm{SEU}}$, per bit for all the registers and different periods, measured using two beam intensities, $I$. The 64 registers are combined in four groups numbered from 1 to 4. The uncertainties assigned to the measurement from indivdual periods combines the statistical error with the uncertainty on the beam intensity. The uncertainty on the average is computed using the spread of the individual measurements.  $I~{}[\mathrm{n}{\mathrm{c}{}}^{-2}]$ | Period | Group | $\sigma_{\mathrm{SEU}}~{}[10^{-14}\mathrm{c}{}^{2}]$
---|---|---|---
$3.1\pm 0.1$ | period 1 | 1 | $7.97\pm 1.13$
|  | 2 | $6.37\pm 1.01$
|  | 3 | $5.74\pm 0.96$
|  | 4 | $7.01\pm 1.06$
| Average |  | $6.77\pm 0.41$
$299.9\pm 1.3$ | period 1 | 1 | $6.88\pm 0.25$
|  | 2 | $7.50\pm 0.25$
|  | 3 | $7.30\pm 0.25$
|  | 4 | $7.11\pm 0.25$
| period 2 | 1 | $8.52\pm 0.36$
|  | 2 | $7.74\pm 0.34$
|  | 3 | $8.64\pm 0.36$
|  | 4 | $8.61\pm 0.36$
| period 3 | 1 | $7.60\pm 0.12$
|  | 2 | $7.75\pm 0.12$
|  | 3 | $7.82\pm 0.12$
|  | 4 | $7.89\pm 0.12$
| period 4 | 1 | $8.21\pm 0.30$
|  | 2 | $8.48\pm 0.31$
|  | 3 | $8.13\pm 0.30$
|  | 4 | $8.20\pm 0.30$
| Average |  | $7.90\pm 0.13$
Figure 4: SEU cross-section computed every second for the four groups (row) of
the four periods (column). The red line corresponds to the fit result over the
whole period. The index of the group and period and the fitted value are
specified on top of each sub-figure.

###  3.2 Distribution of time duration between two errors

The probability that SEE occurs is assumed to be constant. In that case, one
can show that the distribution of the time between two consecutive errors
follows a decreasing exponential

$p(\Delta t)\propto\exp(-\Delta t/\tau)$ (3.1)

where $\tau^{-1}$ corresponds directly to the probability. Looking at the
$\Delta t$ allows then to verify the assumption of a constant probability, and
to extract this probability.

In practice, error counters of every single bit are incremented every
$25\,\mathrm{n}$, but these individual counter values are summed to form one
counter per group, every $\sim 8\,\mathrm{m}$. These values are read out and
saved every $10\,\mathrm{m}$. Therefore, the smallest measured $\Delta t$
value is $10\,$ms. A counter incrementation translates the number of errors,
$n$, that occurred during this time window. When there is more than one error
$n>1$ in a time window, it is assumed that those errors are equally
distributed in time, meaning there are $n$ time intervals of $10/n\,$ms each.
The distribution of $n$ for all the $10\,$ms time windows, and the
distribution of the time interval $\Delta t$ for which the counter
incrementation is at least one are shown on Fig. 5.

The distribution and the fitted exponential function for all analyzed data are
shown on Fig. 6. As for Section 3.1, the final value and uncertainty are taken
from the average over the various datasets. The obtained value of $\tau$
corresponds a SEU cross-section of
$\sigma_{\mathrm{SEU}}^{1{\rightarrow}0}\,=\,(7.44\pm
0.16)\,10^{-14}\mathrm{c}{}^{2}$. This value depends on the number of bins
chosen to fit the $\Delta t$ distribution, increasing by $12\%$ when moving
from 30 bins (used for the above value) to 100 bins. As a consequence, this
result is compatible with the measurement described in Section 3.1.

Figure 5: Time difference between two consecutive errors (left) and error
multiplicity between two consecutive errors (right).  Figure 6: SEU cross-
section computed from exponential fit of $\Delta t$ distribution for the four
groups (line) of the four periods (column). The red line corresponds to the
fitted exponential distribution. The index of the group and period as well as
the fitted cross-section values are specified on top of each sub-plot.

###  3.3 Auto-correction failure due to time-overlapping SEU

Table 2:  Predicted number of auto-correction failures due to uncorrelated SEUs, assuming an instantaneous fluence of $10^{8}~{}\mathrm{n_{eq}^{1~{}}}\,\mathrm{c}{}^{-2}\,{}^{-1}$. $N_{\mathrm{HL-LHC}}^{\mathrm{trip~{}bit}}$ is the expected number of auto-correction failures for one triplicated-bit. $N_{\mathrm{HL-LHC}}^{\mathrm{ASIC}}$ is the expected number of auto-correction failures for a 7200 triplicated-bits bits ASIC during 1 day.  | $N_{\mathrm{HL-LHC}}^{\mathrm{trip-bit}}$ | $N_{\mathrm{HL-LHC}}^{\mathrm{ASIC}}$
---|---|---
$1\rightarrow 0$ | $(4.0\pm 0.1)~{}10^{-13}$ | $(2.9\pm 0.1)~{}10^{-9}$
$0\rightarrow 1$ | $(3.0\pm 0.4)~{}10^{-13}$ | $(2.1\pm 0.3)~{}10^{-9}$

One mechanism that induces auto-correction failures is when two replicas of a
single bit are affected by two SEUs during the correction cycle of
$25~{}\mathrm{n}{}$. The rate of the auto-correction failure can be deduced
from the SEU rate. The probability of two cell flips within a triplicated bit
system can be written as:

$P=C^{2}_{3}\int_{0}^{T_{c}}\sigma_{\mathrm{SEU}}\,f\,dt\,\int_{0}^{T_{c}}\,\sigma_{\mathrm{SEU}}\,f\,dt=3\sigma_{\mathrm{SEU}}^{2}\,f^{2}\,T_{c}^{2}$
(3.2)

where $T_{c}$ is equal to $25~{}\mathrm{n}{}$ and $f$ the instantaneous
fluence. Inner detectors in the ATLAS [9] and CMS [10] experiments will be
exposed to a typical integrated fluence of $10^{15}\mathrm{n_{eq}^{1~{}}}$
[11, 12] during the HL-LHC. As indicated by equation (3.2), uncorrelated
double flips depend quadratically on the intentaneous fluence. The auto-
correction failure rate is estimated assuming an instantaneous fluence of
$10^{8}\mathrm{n_{eq}^{1~{}}}{}^{-1}$ and given in Table 2. Each ASIC will
have a negligible number of alterations of its registers during its lifetime
due to uncorrelated double bit flips.

###  3.4 Observation of auto-correction failures

The auto-correction failure rate was measured by seeking changes in the 64
register contents. Three measurements were performed with high intensity
proton beam, using three register configurations:

  * •

registers filled with 1,

  * •

registers filled with a 11111110 frame,

  * •

registers filled with 0.

Figure 7: View of the ASIC where the flip-flops are highlighted by white
rectangles. The 9 registers where a data corruption was observed are shown.

The associated countings are provided in Table 3. Significant statistical
uncertainties are assigned to this measurement due to the small number of
total errors. When no error is measured, a limit is set with a 95% confidence
level 111It can be shown that an upper limit of $\simeq{3}$ is assigned to the
counting of 0 under a Poisson law with a confidence level of 95%.. It was
checked that the failures affect single bits of different registers with no
identified pattern. The location of these registers is shown on Fig. 7. The
observed rate was extrapolated to HL-LHC integrated luminosity assuming a
quadratic dependence to the fluence, as described by equation (3.2). Comparing
Table 2 and 3, one can see that the observed $0\rightarrow 1$ uncorrected
errors are of the order of $10^{6}$ larger than what is expected from double
SEUs. This rate suggests that other mechanisms should be considered, including
Single Event Transients (SET). Additional measurements should be done in the
future to confirm this rate.

Table 3:  Number of auto-correction failures over all the registers, measured using three data frame configurations. $N_{\mathrm{HL-LHC}}^{\mathrm{trip-bit}}$ ($N_{\mathrm{HL-LHC}}^{\mathrm{ASIC}}$) is the expected number of failures per triplicated-bit (per 7200-triplicated bits ASIC) when extrapolating the observed errors to the HL-LHC fluence. Frame | Duration [] | Errors | $N_{\mathrm{HL-LHC}}^{\mathrm{trip-bit}}$ | $N_{\mathrm{HL-LHC}}^{\mathrm{ASIC}}$
---|---|---|---|---
11111111 | $610$ | $0$ | $<1.1~{}10^{-7}$ | $<0.8~{}10^{-3}$
11111110 | $740$ | $0$ | $<0.9~{}10^{-7}$ | $<0.7~{}10^{-3}$
00000000 | $530$ | $9$ | $(3.9\pm 1.3)~{}10^{-7}$ | $(2.9\pm 0.9)~{}10^{-3}$

###  3.5 Cross-checks based on simulation

The simulation randomly generates SEE events every $1\,$ms for each of the
1536 bits, with a given probability. Every $8\,\mathrm{m}$, the counters of
individual bits are summed together to form the four general counters, as in
the experimental data. Every $10\,$ms, the cumulative error counts are written
for each of these four counters.

Figure 8 shows a comparison between the observed data and the simulation for
three observables, namely the number of errors as a function of time, the
distribution of the time between two consecutive errors, and the error
multiplicity between two consecutive errors. The assumption of a time-
independent probability of error occurrence describes quite well the observed
data. However, the simulation does not explain the error rate at a very short
time since the observed number of 4 errors in $10\,$ms interval is much larger
than the predicted one. This observation goes in the same direction as the
tension mentioned in Section 3.4, where the number of explicit double errors
measured every $25\,$ns is much higher than the double error rate computed
from the single error probability.

Figure 8: Comparison between data and simulation for the number of error as
function time (left), error multiplicity between two consecutive errors
(middle) and $\Delta t$ distribution (right).

##  4 Conclusion

An ASIC prototype was designed to monitor the impact of radiation-induced
errors on a $130~{}\mathrm{n}{}$ technology. It was exposed to a high-
intensity proton beam. Using specific counters, the SEU cross-section was
measured using $62~{}$ protons considering both $1\rightarrow{0}$ and
$0\rightarrow{1}$ bit flip observations. It was also shown that SEEs can be
significantly mitigated by triple modular redundancy. While the single error
rate obtained with a 130 nm TSMC technology follows the expectations, the
significant uncorrected error rate observed in the direct measurement requires
further studies. A new prototype with an improved implementation of the
triplication using the latest TMRG tools will be designed and tested.

## Acknowledgements

We would like to thank the MEDICYC staff from the Centre Antoine Lacassagne
for their availability and their precious help before and during the
irradiation period.

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