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\DOI{10.5802/crmeca.310}
\datereceived{2025-02-21}
\dateaccepted{2025-06-16}
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\dateposted{2025-08-20}
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\title{An introduction to natural radiations and their detrimental
effects on electronic devices}

\alttitle{Une introduction aux effets des radiations naturelles et
leurs effets ind\'{e}sirables sur les dispositifs \'{e}lectroniques}

\author{\firstname{Fr\'ed\'eric} \lastname{Wrobel}\CDRorcid{0000-0002-2437-1223}}
\address{Montpellier University, IES UMR-CNRS5214, France}
\email{frederic.wrobel@umontpellier.fr}

\begin{abstract}
Modern electronic devices are naturally exposed to radiative
environments, particularly in aerospace and avionics applications. This
radiation is a major challenge, as it can lead to breakdowns and
long-term degradation of electronic components. It comes from galactic
cosmic rays (GCRs), the Sun and the Earth's radiation belts. They give
rise to three main families of malfunctions: Total Ionizing Dose (TID),
Displacement Damage (DD) and Single Event Effects (SEEs).

SEEs, in particular Single Event Upsets (SEUs), are of particular
concern, as a single particle can alter the information in a memory
bit, leading to critical errors. Monte Carlo simulations are widely
used to predict and mitigate these effects. These probabilistic methods
can be used to estimate SEU rates and develop hardening strategies.
Monte Carlo predictive tools simulate the interaction of particles with
electronic devices to assess their vulnerability.

Although Monte Carlo simulations have their limitations, particularly
in modeling sensitive volumes and nuclear interactions, they remain
essential for the aerospace and automotive industries. With the
miniaturization of components, susceptibility to SEUs is increasing,
underlining the need to improve these prediction tools and to pursue
research into the reliability of components exposed to radiation.
\end{abstract}

\begin{altabstract}
Les dispositifs \'{e}lectroniques modernes sont naturellement
expos\'{e}s \`{a} des environnements radiatifs, notamment dans
l'a\'{e}rospatial et les applications avioniques. Ces radiations
constituent un d\'{e}fi majeur, car elles peuvent provoquer des pannes
et une d\'{e}gradation \`{a} long terme des composants
\'{e}lectroniques. Elles proviennent des rayons cosmiques galactiques
(GCRs), du Soleil et les ceintures de radiations terrestres. Elles
engendrent trois familles de dysfonctionnements principaux : la dose
ionisante totale (TID), les effets de d\'{e}placement (DD) et les
effets singuliers (SEEs).

Les SEEs, en particulier les Single Event Upsets (SEUs), sont
pr\'{e}occupants, car une seule particule peut modifier l'information
d'un bit de m\'{e}moire, entra\^{i}nant des erreurs critiques. Pour
pr\'{e}dire et att\'{e}nuer ces effets, les simulations de type Monte
Carlo sont largement utilis\'{e}es. Ces m\'{e}thodes probabilistes
permettent d'estimer les taux de SEU et d'\'{e}laborer des
strat\'{e}gies de durcissement. Les outils, dits de pr\'{e}diction,
simulent l'interaction des particules avec les dispositifs
\'{e}lectroniques afin d'\'{e}valuer leur vuln\'{e}rabilit\'{e}. 

Bien que les simulations Monte Carlo pr\'{e}sentent des limites, notamment
dans la mod\'{e}lisation des volumes sensibles et des interactions
nucl\'{e}aires, elles restent essentielles pour l'industrie
a\'{e}rospatiale et automobile. Avec la miniaturisation des composants,
la susceptibilit\'{e} aux SEUs augmente, soulignant la
n\'{e}cessit\'{e} d'am\'{e}liorer ces outils de pr\'{e}diction et de
poursuivre les recherches sur la fiabilit\'{e} des composants
expos\'{e}s aux radiations.
\end{altabstract}

\keywords{\kwd{Radiations}\kwd{Single Event Upset}\kwd{Monte Carlo
simulations}\kwd{Electronic reliability}}

\altkeywords{\kwd{Radiations}\kwd{Ev\'{e}nement
Singulier}\kwd{Simulations Monte Carlo}\kwd{Fiabilit\'{e}
\'{e}lectronique}}

\maketitle

\end{noXML}    

\section{Introduction}\label{sec1}

Electronic devices are increasingly being used in environments where
they are exposed to extreme conditions, such as vacuum, intense
vibrations, wide temperature variations, and high levels of radiation.
While the effects of vacuum and temperature are well understood and
often mitigated using traditional engineering approaches, radiations
are still a crucial challenge, particularly in aerospace, nuclear, and
high-altitude applications. Radiations are not limited to artificial
sources like nuclear reactors or particle accelerators. They are indeed
present in a natural environment such as Space and the atmosphere. In
these environments, electronic components are likely to undergo
failures and long-term degradation~\cite{1}.

This paper explores these environments, which impact electronic
systems. Natural environment radiations include galactic cosmic rays
(GCRs), solar energetic particles (SEPs) and Earth's radiation belts.
The particles that compose these environments interact with electronic
materials, leading to various effects such as Total Ionizing Dose
(TID), Displacement Damage (DD), and Single Event Effects (SEEs). Among
SEE, Single Event Upsets (SEUs) are of the main concern since they are
due to a unique particle that deposits enough energy in the device to
flip a memory bit~\cite{2}.

Predicting and mitigating SEU-induced failures is of crucial interest
and must be deeply investigated. Monte Carlo simulations tools are
widely used in this field as they allow to assess SEU rates and develop
hardening techniques. This paper discusses the use of these Monte Carlo
tools, including their advantages and limitations.\vspace*{-2pt}

\section{The natural radiative environment}\label{sec2}

Radiations are everywhere around us, from space down to our planet.
Radiations have a major impact on both living organisms and electronic
systems. In this part, we present the origin of these radiations that
include galactic cosmic rays, solar radiations and the terrestrial
environment.\vspace*{-2pt}

\subsection{Galactic cosmic rays}\label{ssec21}

Galactic cosmic rays (GCRs) are composed of high-energy particles
present in Space. They are mainly composed of protons, electrons, and
heavy ions. These particles come from astrophysical events, such as
supernova explosions that can accelerate particles~\cite{3}.\vspace*{-2pt}

\subsection{Solar radiative environment}\label{ssec22}

The Sun emits unpredictable and powerful bursts of radiation. This
emission follows an 11-year activity cycle, during which the flux of
particles varies. Solar flares and coronal mass ejections (CMEs)
release large amounts of high-energy particles such as protons and
helium nuclei~\cite{4}. These solar energetic particles (SEPs) can
induce failures in electronic devices onboard our satellites. In
addition to these bursts of radiation, the Sun emits a continuous flux
of particles, which is called solar wind and mainly composed of protons
and electrons.\vspace*{-2pt}

\subsection{Terrestrial space environment}\label{ssec23}

The Earth's magnetic field traps some of the charged particles that
comes from space, creating the Van Allen radiation belts. These belts
contain high flux of protons and electrons, which is an additional risk
for embedded electronics that fly through these regions. The Van Allen
radiation belts, or radiation belts, are composed of two belts: the
inner belt is composed of energetic protons and electrons, while the
outer belt is mainly composed of electrons. During solar storms,
radiation belts can expand, exposing then satellites to higher
radiation levels~\cite{5,6}.

\subsection{Radiative environment in the atmosphere}\label{ssec24}

When cosmic rays reach the top of the atmosphere, they interact with
the nuclei of the atoms and produce secondary particles, which can
penetrate deep into the atmosphere and even reach the ground level.
While many kinds of particles are produced during these interactions
(protons, electrons, muons, neutrons\,{\ldots}), neutrons represent the
main risk for electronic devices. Their flux varies with altitude. For
example, the flux at ground level is known to be around 
20~n/cm\tsup{2}/h and is 300 times higher at avionic altitude
(typically 12~km). 

\section{Radiation effects on electronic devices}\label{sec3}

Radiation effects on electronic devices can be divided into three major
categories: Total Ionizing Dose (TID), Displacement Damage (DD), and
Single Event Effects (SEEs). Each type of damage impacts electronic
devices differently.

\subsection{Total Ionizing Dose (TID) effects}\label{ssec31}

Prolonged exposure to radiation leads to a progressive degradation of
the functionality of electronic components. This phenomenon, known as
total ionizing dose (TID), results in an accumulation of charges in
insulating materials such as silicon dioxide. As a result, transistors
and electronic circuits lose performance and can become
unusable~\cite{7}.

To decrease the effects of TID, it is possible to use specific
materials, hardening techniques and circuit designs that minimize the
impact of radiation. For example, space electronics are manufactured
using radiation-hardened transistors~\cite{8}.

\subsection{Displacement Damage (DD) effects}\label{ssec32}

When particles interact with semiconductor material, they can knock
atoms out of their sites, modifying the structure of the crystal and
affecting the properties of the semiconductor. This process, known as
Displacement Damage (DD), reduces the efficiency of semiconductor
devices such as solar cells and image sensors~\cite{9}.

Optoelectronic components, such as infrared detectors and fiber-optic
communication systems, are particularly vulnerable to DD effects. To
mitigate this damage more resistant semiconductors may be used such as
silicon carbide (SiC) and gallium nitride (GaN)~\cite{10}.

\subsection{Single Event Effects (SEEs)}\label{ssec33}

Unlike Total Ionizing Dose (TID) and Displacement Damage (DD), which
result from cumulative exposure over time, Single Event Effects (SEEs)
occur when a single particle strikes a sensitive region of a
transistor~\cite{1,2,11}. This may lead to data corruption but also to
catastrophic\break failures.

One of the most common SEEs is the Single Event Upset (SEU), when a
charged particle deposits energy in a memory cell, leading to a bit
flip (data corruption). For critical systems, this necessitates
mitigation techniques such as error correction codes (ECC) and
redundant memory architectures. Another type of SEE is the Single Event
Latchup (SEL)~\cite{12}, in which an ionizing particle triggers a
parasitic thyristor, which creates a low-resistance path that can lead
to a destructive short circuit. To prevent SEL, it is possible to use
current-limiting circuitry and radiation-hardened design strategies.

Other catastrophic SEEs include Single Event Burnout (SEB)~\cite{13}
and Single Event Gate Rupture (SEGR)~\cite{14} which affect power
devices. SEB occurs when a high-energy particle induces excessive
current in a power transistor, causing thermal runaway and permanent
failure. SEGR, on the other hand, is the rupture of an insulating gate
oxide layer due to excessive electric fields, leading to a
non-recoverable breakdown of the component. 

To improve electronic reliability in radiation-exposed applications,
space agencies and industry conduct extensive testing under beam of
particles. Techniques such as triple modular redundancy (TMR) and
real-time error detection further enhance system robustness against
SEEs. The continuous advancement in radiation-hardened materials and
circuit design plays a crucial role in mitigating these effects.

\section{The special case of single event effects}\label{sec4}
\subsection{Monte Carlo simulations for SEU analysis}\label{ssec41}

Single Event Upsets (SEUs) are one of the most concerning radiation
effects in modern technologies, particularly in space and avionics
applications. Because SEUs occur randomly when a particle ionizes a
sensitive region of a semiconductor device, their prediction requires
probabilistic methods. Monte Carlo simulations are widely used for
estimating the SEU cross-section (a quantity that assess the
sensitivity of the device) and predicting the Soft Error Rate (SER) of
a given technology.

Monte Carlo methods are based on random numbers to model the
interactions between radiation and matter of electronic devices.
Instead of using deterministic equations to calculate SEU
probabilities, these simulations generate thousands of millions of
random particle interactions based on physical probability
distributions. We can then obtain statistically significant estimates
of SEU rates under a given radiation environment.

\subsection{Typical Monte Carlo steps for SEU prediction}\label{ssec42}

Many companies have developed their own Prediction tool in order to
assess the reliability of a given device in a given environment. The
good metric for such an assessment is the SEU cross section which gives
the equivalent area of the device that is sensitive to radiation. These
tools are always proprietary, and it is thus difficult to know exactly
all the key ingredients that are inside. Very often, these tools are
based on Monte Carlo approach, which basically includes the following
steps:

\noindent
(1) {Circuit definition:} The user defines the circuit to be
studied. It is composed of NMOS and PMOS transistors for which the
parameters must be defined (gate length, gate width, supply voltage).
Figure~\ref{fig1} shows an example of circuit definition for a
4-transistor SRAM cell.

\begin{figure}
\includegraphics{fig01}
\caption{\label{fig1}Example of circuit definition of an SRAM Cell with
no access transistors. Green disks indicate the ON transistors (no
sensitivity to SEU). Red disks indicate the OFF transistors
(sensitivity to SEU).}
\end{figure}

\noindent
(2) {Layout definition:} The user defines the location of each
transistor as well as its size (drains and sources width). Average
doping may also be accounted for. Figure~\ref{fig2} shows an example of
the layout associated with the SRAM cell (4 transistors).

\begin{figure}
\vspace*{4pt}
\includegraphics{fig02}
\caption{\label{fig2}Example of layout definition of an SRAM Cell with
no access transistors. Pink boxes indicate the gates. Red boxes are
electrodes connected with power supply ($\mathrm{V}_{\mathrm{DD}}$).
Grey boxes indicate electrodes connected to ground. Yellow boxes are
other PMOS electrodes. Green boxes are other NMOS electrodes.}
\end{figure}

\noindent
(3) {Geometry definition:} A basic structure is made of a bulk of
silicon and a BEOL in silicon dioxide. The thickness of the bulk and
that of the BEOL must be defined. In addition, the user must choose the
direction of the beam of particles that will irradiate the device.
Figure~\ref{fig3} shows an example of geometry in which the irradiated
layout is embedded.

\begin{figure}
{\vspace*{-2pt}}
\includegraphics{fig03}
{\vspace*{-2pt}}
\caption{\label{fig3}Example of geometry definition indicating the
irradiated layout and the beam direction.}
{\vspace*{2pt}}
\end{figure}

\noindent
(4) {Parameters:} The user chooses the type of particles between
ions, neutrons and protons as well as the criteria for stopping the
simulations (based on the accuracy and the fluence of particles
simulated). Figure~\ref{fig4} shows a typical user interface for the
beam parameters.

\begin{figure}
\includegraphics{fig04}
{\vspace*{-2pt}}
\caption{\label{fig4}Typical parameter interface of a predictive Monte
Carlo tool.}
{\vspace*{-2pt}}
\end{figure}

\noindent
(5) {Running:} Once all previous steps are achieved, the
simulation may be run. The tool graphically shows the cross section of
SEU as a function of stopping power (for ions) or energy (for neutrons
and protons). At the beginning of the simulation, due to poor
statistic, results do not seem consistent. Then, with the increasing
number of events that are simulated, the curve converges toward a
result (an example is provided in Figure~\ref{fig5}). It shows an
increase in the cross section, which indicates that the sensitivity of
the device increases with neutron energy and reaches a plateau above a
few tens of MeV. The cross-section value is in the order of $10^{-14}$
cm\tsup{2}/bit. It means that the SEU will be triggered if the neutron
reaches this area. The probability of such an event seems to be very
low, and it is. Indeed, knowing that the flux of neutrons at ground
level is around 20~n/cm\tsup{2}/h, we should expect $2\times 10^{-13}$
SEU per hour. However, this is the sensitivity associated with a single
bit, while memories are often composed of Gbit. Additionally, if we
consider avionics applications, the number of SEU is multiplied by a
factor of 300 compared to ground level. For example, a 1~GBytes
memories in a commercial flight will undergo 1 SEU every 2~h of flight
at 12~km of altitude. 

\begin{figure}
\includegraphics{fig05}
\caption{\label{fig5}Typical results obtained with a Monte Carlo
predictive tool. The cross section represents the sensitivity of the
device to particles (here neutrons). Dots are simulation points, and
the line is a fit with a Weibull shape, generally used to this
purpose.} 
\end{figure}

\subsection{Key assumptions and limitations of Monte
Carlo methods}\label{ssec43}

Monte Carlo simulations, while highly useful for predicting Single
Event Upsets (SEUs), rely on certain assumptions that can impact their
accuracy. For example, in some tools, one key assumption is the
modeling of the sensitive region within the semiconductor device. In
many simulations, the volume of this region is approximated using
simplified geometric shapes, such as rectangular parallelepipeds, which
may not fully represent the actual charge collection and does not
provide any information about the time dependence of the process.
Another critical factor is the choice of nuclear reaction models, which
determine how incident particles interact with semiconductor. The
accuracy of SEU predictions strongly depends on the nuclear physics
used. Additionally, modern semiconductor devices exhibit charge sharing
between neighboring transistors, leading to multiple cell upsets
(MCUs). While some Monte Carlo methods attempt to incorporate
charge-sharing effects, accurately capturing these phenomena remains a
challenge due to the complexity of semiconductor physics. Despite these
limitations, Monte Carlo simulations remain one of the most effective
tools for understanding SEU behavior, provided that their assumptions
are well-calibrated against experimental data.

\subsection{Advantages of Monte Carlo SEU simulations}\label{ssec44}

Monte Carlo-based simulations have become indispensable for assessing
the vulnerability of electronic components to radiation-induced SEUs.
One of their major advantages is cost reduction, as they lower the need
for extensive physical testing in radiation facilities, which are
expensive and time-consuming. Additionally, Monte Carlo methods allow
to simulate different conditions without requiring physical hardware
modifications. This is particularly useful for space applications,
where predicting SEU rates under varying solar and galactic cosmic ray
conditions is essential. Another key benefit is scalability: as
semiconductor technology advances, device structures become
increasingly complex, making traditional testing methods less
practical. Also, Monte Carlo simulations enable to analyze new
materials and transistor architectures efficiently. The continued
refinement of these simulation tools, combined with experimental
validation, makes them an essential tool for space agencies,
semiconductor manufacturers, and avionics engineers working on
radiation-hardened electronics.

\section{Conclusion}\label{sec5}

The study of radiation effects on electronic devices is critical for
ensuring the reliability of modern technology, particularly in space,
avionics, and high-altitude applications. The natural radiative
environment, composed of galactic cosmic rays, solar radiation, and
trapped particles in the Earth's magnetosphere, presents significant
challenges for electronic components. These radiation sources can
induce a range of failures, such as Total Ionizing Dose (TID),
Displacement Damage (DD) and Single Event Effects (SEEs).

Among SEEs, Single Event Upsets (SEUs) pose a unique challenge due to
their stochastic nature. Understanding and predicting SEUs require
sophisticated modeling techniques, and Monte Carlo-based simulations
have emerged as powerful tools in this domain. These simulations allow
to assess the probability of SEUs under various radiation conditions,
evaluate the sensitivity of devices, and optimize mitigation
techniques. Despite certain assumptions and limitations, Monte Carlo
methods provide a complementary tool to extensive radiation testing,
making them a crucial resource for industries.

As technology advances and transistor sizes continue to shrink, the
susceptibility of electronic devices to radiation-induced failures is
expected to increase. This trend underscores the importance of
continued research in radiation effects modeling, error correction
techniques, and hardened circuit design. Future developments in Monte
Carlo-based prediction tools, coupled with experimental validation,
will be crucial for developing next-generation electronics capable of
withstanding the ever-present challenges of radiation exposure in
critical applications.

\section*{Declaration of interests}

The author does not work for, advise, own shares in, or receive funds
from any organization that could benefit from this article, and has
declared no affiliations other than their research organization.

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\end{document}
