FPGAs are frequently used in network systems to provide the performance and flexibility that is required of modern computer networks while allowing network vendors to bring products to market quickly. Like all electronic devices, FPGAs are vulnerable to ionizing radiation which can cause applications operating on an FPGA to fail. These low-level failures can have a wide range of negative effects on the performance of a network system. As computer networks play a larger role in modern society, it becomes increasingly important that these soft errors are addressed in the design of network systems.This work presents a framework for testing the soft-error reliability of FPGA-based networking systems. The framework consists of the NetFPGA development board, a custom traffic generator, and a custom high-speed JTAG configuration device. The NetFPGA development board is versatile and can be used to implement a wide range of network applications. The traffic generator is used to exercise the network system on the NetFPGA and to determine the health of that system. The JTAG configuration device is used to manage reliability experiments, to perform fault injection into the FPGA, and to monitor the NetFPGA during radiation tests.This thesis includes soft-error reliability tests that were performed on an Ethernet switch network system. Using both fault injection and accelerate radiation testing, the soft error sensitivity of the Ethernet switch was measured. The Ethernet switch design was then mitigated using triple module redundancy and duplication with compare. These mitigated designs were also tested and compared against the baseline design. Radiation testing shows that TMR provides a 5.05x improvement in reliability over the baseline design. DWC provides a 5.22x improvement in detectability over the baseline design without reducing the reliability of the system.

Artificial neural networks (ANNs) are used in many types of computing applications. Traditionally, ANNs have been implemented in software, executing on CPUs and even GPUs, which capitalize on the parallelizable nature of ANNs. More recently, FPGAs have become a target platform for ANN implementations due to their relatively low cost, low power, and flexibility. Some safety-critical applications could benefit from ANNs, but these applications require a certain level of reliability. SRAM-based FPGAs are sensitive to single-event upsets (SEUs), which can lead to faults and errors in execution. However there are techniques that can mask such SEUs and thereby improve the overall design reliability. This thesis evaluates the SEU reliability of neural networks implemented in SRAM-based FPGAs and investigates mitigation techniques against upsets for two case studies. The first was based on the LeNet-5 convolutional neural network and was used to test an implementation with both fault injection and neutron radiation experiments, demonstrating that our fault injection experiments could accurately evaluate SEU reliability of the networks. SEU reliability was improved by selectively applying TMR to the most critical layers of the design, achieving a 35% improvement reliability at an increase in 6.6% resources. The second was an existing neural network called BNN-PYNQ. While the base design was more sensitive to upsets than the CNN previous tested, the TMR technique improved the reliability by approximately 7× in fault injection experiments.

A fault injection tool has been created to emulate single event upset (SEU) behavior within the configuration memory of an FPGA. This tool is able to rapidly and accurately determine the dynamic sensitive cross section of the configuration memory for a given FPGA design. This tool enables the reliability of FPGA designs and fault tolerance schemes to be quickly and accurately tested. The validity of testing performed with this fault injection tool has been confirmed through radiation testing. A radiation test was conducted at Crocker Nuclear Laboratory using a proton accelerator in order to determine the actual dynamic sensitive cross section for specific FPGA designs. The results of this radiation testing were then analyzed and compared with similar fault injection tests, with results suggesting that the fault injection tool behavior is indeed accurate and valid. The fault injection tool can be used to determine the sensitivity of an FPGA design to configuration memory upsets. Additionally, fault mitigation techniques designed to increase the reliability of an FPGA design in spite of upsets within the configuration memory, can be thoroughly tested through fault injection. Fault injection testing should help to increase the feasibility of reconfigurable computing in space. FPGAs are well suited to the computational demands of space based signal processing applications; however, without appropriate mitigation or redundancy techniques, FPGAs are unreliable in a radiation environment. Because the fault injection tool has been shown to reliably model the effects of single event upsets within the configuration memory, it can be used to accurately evaluate the effectiveness of fault tolerance techniques in FPGAs.

This dissertation studies the effects of radiation-induced single-event upsets (SEUs) on field-programmable gate array(FPGA)-based circuits. It analyzes and quantifies a special case in data communication, that is, the synchronization issue of signals when they are sent across clock domains in triple modular redundancy (TMR) circuits with the presence of SEUs. After demonstrating that synchronizing errors cannot be eliminated in such case, this dissertation continues to present novel synchronizer designs that can guarantee reliable synchronization of triplicated signals. Fault injection tests then show that the proposed synchronizers provide between 6 and 10 orders of magnitude longer mean time to failure (MTTF) than unmitigated synchronizers. This dissertation also studies the reliability of block random access memory (BRAM) on FPGAs. By investigating several previous reliability models for single-error correction/double-error detection (SEC/DED) memory with scrubbing, this dissertation proposes two novel MTTF models that are suitable for FPGA applications. The first one considers non-uniform write rates for probabilistic write scrubbing, and the second one combines deterministic scrubbing and probabilistic scrubbing into a single model. The proposed models reveal the impact of memory access patterns on the reliability of BRAMs. Monte Carlo simulations then demonstrate the correctness of the proposed models. At last, the memory access patterns of a type of FPGA application, digital signal processing (DSP) is studied, and mitigation mechanisms for DSP applications are discussed.

Deep learning is replacing many traditional data processing methods in computer vision, speech recognition, natural language processing and many more diverse end applications. Until only a few years ago, using deep learning networks for inference required large amount of computational resources such as memory, processing power and energy. It was not trivial to deploy the computationally-expensive deep neural networks on embedded devices with limited capabilities. In recent years however, deep learning is finding its way through the world of embedded devices. Embedded systems such as Internet of Things (IoT) devices, phones and even components in cars are being equipped with deep neural networks. This raises interesting challenges for both embedded designers and deep learning scientists to close the gap between these two domains. In this thesis work, some challenges involved in deploying deep learning for embedded systems were discussed, as well as some of the available solutions and frameworks. Moreover, focusing on the safety and fault-tolerance aspects of embedded systems, tolerance of deep neural networks against faults was investigated using an experiment-based research strategy. A fault injection framework was designed and implemented that targeted deep neural networks defined using PyTorch. The framework developed was used to perform fault injection experiments on a small deep learning network. It was found how faults have various impacts on the accuracy of the neural network depending on the type of layer targeted by the faults. Worst-case faults were identified and several architectural modifications on the deep neural network were examined to improve the fault tolerance of the neural network under study. / Djupinlärning ersätter många traditionella databehandlingsmetoder inom datorseende, röstigenkänning, språkteknologi och flera olika slutanvändningar. Fram tills några år sedan har användningen av djupinlärningsnätverk för inferens krävt stora mängder beräkningsresurser såsom minne, processorkraft och energi. Det är inte trivialt att distribuera det beräknings-dyra djupa neurala nätverket på inbyggda enheter med begränsad kapacitet. Under de senaste åren har dock djupinlärning nått världens inbyggda enheter. Inbyggda system som Internet of Things (IoT) -enheter, telefoner och till och med komponenter i bilar utrustas med djupa neurala nätverk. Detta medför intressanta utmaningar för både designers av inbyggda system och djupinlärningsforskare att minska klyftan mellan dessa två domäner. I detta examensarbete diskuteras några av utmaningarna med att distribuera djupinlärning för inbyggda system, samt tillgängliga lösningar och ramverk. Djupa neurala nätverks feltolerans undersöks. Ett ramverk för felinjicering skapades som riktade sig mot djupa neurala nätverk som använder sig av Pytorch. Ramverket användes sedan för att utföra felinjektionsexperiment på ett mindre djupinlärningsnätverk. Man kunde se hur felinjiceringar påverkade nätverkets noggrannhet på olika sätt beroende på vilket lager av nätverket felinjiceringen gjordes på. Den värsta sortens felinjicering identifierades och olika arkitektoniska modifieringar av det djupa neurala nätverket undersöktes för att förbättra feltoleransen av nätverket.

La miniaturisation des circuits intégrés numériques tend à augmenter leur sensibilité aux radiations. Ainsi le rayonnement naturel peut induire des événements singuliers et porter atteinte à la fiabilité des circuits.Cette thèse porte sur la modélisation des mécanismes à l'origine de ces événements singuliers et sur le développement de solutions de durcissement par conception permettant de limiter l'impact des radiations sur le taux d'erreur.Dans une première partie, nous avons notamment développé une approche dénommée RWDD (Random-Walk Drift- Diffusion) modélisant le transport et la collection de charges au sein d'un circuit, sur la base d'équations physiques sans paramètre d'ajustement. Ce modèle particulaire et sa résolution numérique transitoire permettent de coupler le transport des charges avec un simulateur circuit, tenant ainsi compte de l'évolution temporelle des champs électriques dans la structure. Le modèle RWDD a été intégré avec succès dans une plateforme de simulation capable d'estimer la réponse d'un circuit suite à l'impact d'une particule ionisante.Dans une seconde partie, des solutions de durcissement permettant de limiter l'impact des radiations sur la fiabilité des circuits ont été développées. A l'échelle des cellules élémentaires, de nouvelles bascules robustes aux radiations ont été proposées, en limitant leur impact les performances. Au niveau système, une méthodologie de duplication de l'arbre d'horloge a été développée. Enfin, un flot de triplication a été conçu pour les systèmes dont la fiabilité est critique. L'ensemble de ces solutions a été implémenté en technologie 65 nm et UTBB-FDSOI 28 nm et leur efficacité vérifiée expérimentalement. / The extreme technology scaling of digital circuits leads to increase their sensitivity to ionizing radiation, whether in spatial or terrestrial environments. Natural radiation can now induce single event effects in deca-nanometer circuits and impact their reliability.This thesis focuses on the modeling of single event mechanisms and the development of hardening by design solutions that mitigate radiation threat on the circuit error rate.In a first part of this work, we have developed a physical model for both the transport and collection of radiation-induced charges in a biased circuit, derived from pure physics-based equations without any fitting parameter. This model is called Random-Walk Drift-Diffusion (RWDD). This particle-level model and its numerical transient solving allows the coupling of the charge collection process with a circuit simulator, taking into account the time variations of the electrical fields in the structure. The RWDD model is able to simulate the behavior of a circuit following a radiation impact, independently of the implemented function and the considered technology.In a second part of our work, hardening solutions that limit radiation impacts on circuit reliability have been developed. At elementary cell level, new radiation-hardened latch architectures have been proposed, with a limited impact on performances. At system level, a clock tree duplication methodology has been proposed, leaning on specific latches. Finally, a triplication flow has been design for critical applications. All these solutions have been implemented in 65 nm and UTBB-FDSOI 28nm technologies and radiation test have been performed to measure their hardening efficiency.

Hydrocarbon exploration, global navigation satellite systems, computed tomography, and aircraft avionics are just a few examples of applications that require system operation at an ambient temperature, pressure, or radiation level outside the range covered by military specifications. The electronics employed in these applications are known as "extreme environment electronics." On account of the increased cost resulting from both process modifications and the use of exotic substrate materials, only a handful of semiconductor foundries have specialized in the production of extreme environment electronics. Protection of these electronic systems in an extreme environment may be attained by encapsulating sensitive circuits in a controlled environment, which provides isolation from the hostile ambient, often at a significant cost and performance penalty. In a significant departure from this traditional approach, system designers have begun to use commercial off-the-shelf technology platforms with built in mitigation techniques for extreme environment applications. Such an approach simultaneously leverages the state of the art in technology performance with significant savings in project cost. Silicon-germanium is one such commercial technology platform that demonstrates potential for deployment into extreme environment applications as a result of its excellent performance at cryogenic temperatures, remarkable tolerance to radiation-induced degradation, and monolithic integration with silicon-based manufacturing. In this dissertation the radiation response of silicon-germanium technology is investigated, and novel transistor-level layout-based techniques are implemented to improve the radiation tolerance of HBT digital logic.

In the thesis the design of the Data Storage System (DSS) is proposed with the respect to the V-Model methodology. The design is based on users requirements, from which the system requirements are created and the technical specification of the DSS is developed. In the technical specifications the functionality of the DMM and HMI DSS subsystems are described and sub-system requirements are assigned to them, then they are subdivided and assigned to individual DMM (Data memory module) and HMI hardware items. Moreover, requirements are analyzed on hardware items, specific electronic components, are selected and implemented into the block design of the DMM hardware. Based on the block design of hardware, the hardware of the DMM subsystem is designed, selectively simulated and implemented along with the printed circuit board. On the implemented hardware of the DMM subsystems measurements are performed in order to verify the basic functionality of the hardware and the calculated, assimilated and measured values are compared as well. At the end of the thesis there is a short description of the implementation of the software design and its use for basic initialization of the selected processor, together with the verification of its basic function - measuring the frequency of the internal clock sources and the clock domains. The work is completed by sending a message of defined parameters to the selected communication line and sapling it by an oscilloscope, so that the basic function of the DMM subsystem is verified.

En la presente tesis se propone una metodología de desarrollo de estrategias híbridas para la mitigación de fallos inducidos por radiación en los sistemas empotrados modernos. La propuesta se basa en los principios del co-diseño de sistemas y consiste en la combinación selectiva, incremental y flexible de enfoques de tolerancia a fallos basados en hardware y software. Es decir, la exploración del espacio de soluciones se fundamenta en una estrategia híbrida de grano fino. El flujo de diseño está guiado por los requisitos de la aplicación. Esta metodología se ha denominado: co-endurecimiento. De esta forma, es posible diseñar sistemas embebidos confiables a bajo coste, donde no sólo se satisfagan los requisitos de confiabilidad y las restricciones de diseño, sino que también se evite el uso excesivo de costosos mecanismos de protección (hardware y software).

En raison de leur impact sur la fiabilité des systèmes, les effets du rayonnement cosmique sur l’électronique ont été étudiés dès le début de l’exploration spatiale. Néanmoins, de récentes évolutions industrielles bouleversent les pratiques dans le domaine, les technologies standard devenant de plus en plus attrayantes pour réaliser des circuits durcis aux radiations. Du fait de leurs fréquences élevées, des nouvelles architectures de transistor et des temps de durcissement réduits, les puces fabriquées suivant les derniers procédés CMOS posent de nombreux défis. Ce travail s’attelle donc à la simulation des aléas logiques permanents (SEU) et transitoires (SET), en technologies FD-SOI et bulk Si avancées. La réponse radiative des transistors FD-SOI 28 nm est tout d’abord étudiée par le biais de simulations TCAD, amenant au développement de deux modèles innovants pour décrire les courants induits par particules ionisantes en FD-SOI. Le premier est principalement comportemental, tandis que le second capture des phénomènes complexes tels que l’amplification bipolaire parasite et la rétroaction du circuit, à partir des premiers principes de semi-conducteurs et en accord avec les simulations TCAD poussées.Ces modèles compacts sont alors couplés à une plateforme de simulation Monte Carlo du taux d’erreurs radiatives (SER) conduisant à une large validation sur des données expérimentales recueillies sous faisceau de particules. Enfin, des études par simulation prédictive sont présentées sur des cellules mémoire et portes logiques en FD-SOI 28 nm et bulk Si 65 nm, permettant d’approfondir la compréhension des mécanismes contribuant au SER en orbite des circuits intégrés modernes / The effects of cosmic radiation on electronics have been studied since the early days of space exploration, given the severe reliability constraints arising from harsh space environments. However, recent evolutions in the space industry landscape are changing radiation effects practices and methodologies, with mainstream technologies becoming increasingly attractive for radiation-hardened integrated circuits. Due to their high operating frequencies, new transistor architectures, and short rad-hard development times, chips manufactured in latest CMOS processes pose a variety of challenges, both from an experimental standpoint and for modeling perspectives. This work thus focuses on simulating single-event upsets and transients in advanced FD-SOI and bulk silicon processes.The soft-error response of 28 nm FD-SOI transistors is first investigated through TCAD simulations, allowing to develop two innovative models for radiation-induced currents in FD-SOI. One of them is mainly behavioral, while the other captures complex phenomena, such as parasitic bipolar amplification and circuit feedback effects, from first semiconductor principles and in agreement with detailed TCAD simulations.These compact models are then interfaced to a complete Monte Carlo Soft-Error Rate (SER) simulation platform, leading to extensive validation against experimental data collected on several test vehicles under accelerated particle beams. Finally, predictive simulation studies are presented on bit-cells, sequential and combinational logic gates in 28 nm FD-SOI and 65 nm bulk Si, providing insights into the mechanisms that contribute to the SER of modern integrated circuits in orbit