Les systèmes électroniques, évoluant dans les environnements spatial et terrestre, sont soumis à un flux de particules d'origine naturelle pouvant induire des dysfonctionnements. Ces particules ont la faculté de provoquer des aléas logiques (SEU) dans les mémoires SRAM. Bien que non destructifs, les SEU peuvent avoir des conséquences sur la sûreté de fonctionnement des équipements dans les applications nécessitant une grande fiabilité (avion, satellite, lanceur, médical, etc.). L'évaluation de la sensibilité de la technologie d'un composant est donc nécessaire afin de prédire la fiabilité d'un système. En environnement atmosphérique, cette sensibilité aux SEU est principalement causée par les ions secondaires issus des réactions nucléaires entre les neutrons et les atomes du composant. En environnement spatial, les protons de forte énergie induisent les mêmes effets que les neutrons de l'environnement atmosphérique.<br />Dans ce travail de recherche, un nouveau code de prédiction du taux de SEU a été développé (MC-DASIE) afin de pouvoir quantifier la sensibilité pour un environnement donné et explorer les mécanismes de défaillances en fonction de la technologie. Ce code permet d'étudier différentes technologies de mémoires SRAM (Bulk et SOI) en environnement neutronique et protonique entre 1 MeV et 1 GeV. Ainsi, MC-DASIE a été utilisé avec l'aide d'expérimentations pour étudier l'effet de l'intégration sur la sensibilité des mémoires en environnement terrestre, une comparaison entre les irradiations neutroniques et protoniques et l'influence de la modélisation du composant cible sur le calcul du taux de SEU.

Process variability is the pivotal factor impacting the design of high yield integrated circuits and systems in deep sub-micron CMOS technologies. The electrical and physical properties of transistors and interconnects, the building blocks of integrated circuits, are prone to significant variations that directly impact the performance and power consumption of the fabricated devices, severely impacting the manufacturing yield. However, the large number of the transistors on a single chip adds even more challenges for the analysis of the variation effects, a critical task in diagnosing the cause of failure and designing for yield. Reliable and efficient statistical analysis methodologies in various design phases are key to predict the yield before entering such an expensive fabrication process. In this thesis, the impacts of process variations are examined at three different levels: device, circuit, and micro-architecture. The variation models are provided for each level of abstraction, and new methodologies are proposed for efficient statistical analysis and design under variation. At the circuit level, the variability analysis of three crucial sub-blocks of today's system-on-chips, namely, digital circuits, memory cells, and analog blocks, are targeted. The accurate and efficient yield analysis of circuits is recognized as an extremely challenging task within the electronic design automation community. The large scale of the digital circuits, the extremely high yield requirement for memory cells, and the time-consuming analog circuit simulation are major concerns in the development of any statistical analysis technique. In this thesis, several sampling-based methods have been proposed for these three types of circuits to significantly improve the run-time of the traditional Monte Carlo method, without compromising accuracy. The proposed sampling-based yield analysis methods benefit from the very appealing feature of the MC method, that is, the capability to consider any complex circuit model. However, through the use and engineering of advanced variance reduction and sampling methods, ultra-fast yield estimation solutions are provided for different types of VLSI circuits. Such methods include control variate, importance sampling, correlation-controlled Latin Hypercube Sampling, and Quasi Monte Carlo. At the device level, a methodology is proposed which introduces a variation-aware design perspective for designing MOS devices in aggressively scaled geometries. The method introduces a yield measure at the device level which targets the saturation and leakage currents of an MOS transistor. A statistical method is developed to optimize the advanced doping profiles and geometry features of a device for achieving a maximum device-level yield. Finally, a statistical thermal analysis framework is proposed. It accounts for the process and thermal variations simultaneously, at the micro-architectural level. The analyzer is developed, based on the fact that the process variations lead to uncertain leakage power sources, so that the thermal profile, itself, would have a probabilistic nature. Therefore, by a co-process-thermal-leakage analysis, a more reliable full-chip statistical leakage power yield is calculated.

The PhD thesis focuses on the always-on low power SRAM memories (essentially low dynamic power) in thin CMOS technology node CMOS 32nm and beyond. It reviews the state of the art of the eSRAM and describes different techniques to reduce the static and dynamic power consumption with respect the variability issue. Main techniques of power reduction are reviewed with their contributions and their limitations. It presents also a discussion about a statistical variability modeling and the variability effects on the yield. An original low power architecture based on 5T-Portless bit-cell is presented, with current mode read/write operations, as an ideal candidate for the always-on SRAM memories. A test chip implementation in CMOS 32nm of the 5T-Porless is designed and a comparison with an existing 6T SRAM memory is presented based on simulation. Some test chip functionality results and power consumption are performed. Finally the conclusion highlights the major contributions of the study and discusses the various simplification assumptions to see possible limitations. It is concluded affirmatively about industrial interest of the 5T-Portless SRAM for always-on embedded applications. Perspectives concern the analytical modeling for statistical behavior of SRAM as the Monte-Carlo approach is no more practicable. The migration of the 5T-Portless SRAM may be already considered in advanced nodes.

Monolithic Active Pixel Sensors (MAPS) are good candidates to be used in High Energy Physics (HEP) experiments for charged particle detection. In the HEP applications, MAPS chips are placed very close to the interaction point and are directly exposed to harsh environmental radiation. This thesis focuses on the study and improvement of the MAPS radiation hardness. The main radiation effects and the research progress of MAPS are studied firstly. During the study, the SRAM IP cores built in MAPS are found limiting the radiation hardness of the whole MAPS chips. Consequently, in order to improve the radiation hardness of MAPS, three radiation hard memories are designed and evaluated for the HEP experiments. In order to replace the SRAM IP cores, a radiation hard SRAM is developed on a very limited area. For smaller feature size processes, in which the single event upset (SEU) effects get significant, a radiation hard SRAM with enhanced SEU tolerance is implemented by an error detection and correction algorithm and a bit-interleaving storage. In order to obtain higher radiation tolerance and higher circuitry density, a dual-port memory with an original 2-transistor cell is developed and evaluated for future MAPS chips. Finally, the radiation hardness of the MAPS chips using new available processes is studied, and the future works are prospected.

The nano-age has already begun, where typical feature dimensions are smaller than 100nm. The operating frequency is expected to increase up to 12 GHz, and a single chip will contain over 12 billion transistors in 2020, as given by the International Technology Roadmap for Semiconductors (ITRS) initiative. ITRS also predicts that the scaling of CMOS devices and process technology, as it is known today, will become much more difficult as the industry advances towards the 16nm technology node and further. This aggressive scaling of CMOS technology has pushed the devices to their physical limits. Design goals are governed by several factors other than power, performance and area such as process variations, radiation induced soft errors, and aging degradation mechanisms. These new design challenges have a strong impact on the parametric yield of nanometer digital circuits and also result in functional yield losses in variation-sensitive digital circuits such as Static Random Access Memory (SRAM) and flip-flops. Moreover, sub-threshold SRAM and flip-flops circuits, which are aggravated by the strong demand for lower power consumption, show larger sensitivity to these challenges which reduces their robustness and yield. Accordingly, it is not surprising that the ITRS considers variability and reliability as the most challenging obstacles for nanometer digital circuits robust design. Soft errors are considered one of the main reliability and robustness concerns in SRAM arrays in sub-100nm technologies due to low operating voltage, small node capacitance, and high packing density. The SRAM arrays soft errors immunity is also affected by process variations. We develop statistical design-oriented soft errors immunity variations models for super-threshold and sub-threshold SRAM cells accounting for die-to-die variations and within-die variations. This work provides new design insights and highlights the important design knobs that can be used to reduce the SRAM cells soft errors immunity variations. The developed models are scalable, bias dependent, and only require the knowledge of easily measurable parameters. This makes them useful in early design exploration, circuit optimization as well as technology prediction. The derived models are verified using Monte Carlo SPICE simulations, referring to an industrial hardware-calibrated 65nm CMOS technology. The demand for higher performance leads to very deep pipelining which means that hundreds of thousands of flip-flops are required to control the data flow under strict timing constraints. A violation of the timing constraints at a flip-flop can result in latching incorrect data causing the overall system to malfunction. In addition, the flip-flops power dissipation represents a considerable fraction of the total power dissipation. Sub-threshold flip-flops are considered the most energy efficient solution for low power applications in which, performance is of secondary importance. Accordingly, statistical gate sizing is conducted to different flip-flops topologies for timing yield improvement of super-threshold flip-flops and power yield improvement of sub-threshold flip-flops. Following that, a comparative analysis between these flip-flops topologies considering the required overhead for yield improvement is performed. This comparative analysis provides useful recommendations that help flip-flops designers on selecting the best flip-flops topology that satisfies their system specifications while taking the process variations impact and robustness requirements into account. Adaptive Body Bias (ABB) allows the tuning of the transistor threshold voltage, Vt, by controlling the transistor body voltage. A forward body bias reduces Vt, increasing the device speed at the expense of increased leakage power. Alternatively, a reverse body bias increases Vt, reducing the leakage power but slowing the device. Therefore, the impact of process variations is mitigated by speeding up slow and less leaky devices or slowing down devices that are fast and highly leaky. Practically, the implementation of the ABB is desirable to bias each device in a design independently, to mitigate within-die variations. However, supplying so many separate voltages inside a die results in a large area overhead. On the other hand, using the same body bias for all devices on the same die limits its capability to compensate for within-die variations. Thus, the granularity level of the ABB scheme is a trade-off between the within-die variations compensation capability and the associated area overhead. This work introduces new ABB circuits that exhibit lower area overhead by a factor of 143X than that of previous ABB circuits. In addition, these ABB circuits are resolution free since no digital-to-analog converters or analog-to-digital converters are required on their implementations. These ABB circuits are adopted to high performance critical paths, emulating a real microprocessor architecture, for process variations compensation and also adopted to SRAM arrays, for Negative Bias Temperature Instability (NBTI) aging and process variations compensation. The effectiveness of the new ABB circuits is verified by post layout simulation results and test chip measurements using triple-well 65nm CMOS technology. The highly capacitive nodes of wide fan-in dynamic circuits and SRAM bitlines limit the performance of these circuits. In addition, process variations mitigation by statistical gate sizing increases this capacitance further and fails in achieving the target yield improvement. We propose new negative capacitance circuits that reduce the overall parasitic capacitance of these highly capacitive nodes. These negative capacitance circuits are adopted to wide fan-in dynamic circuits for timing yield improvement up to 99.87% and to SRAM arrays for read access yield improvement up to 100%. The area and power overheads of these new negative capacitance circuits are amortized over the large die area of the microprocessor and the SRAM array. The effectiveness of the new negative capacitance circuits is verified by post layout simulation results and test chip measurements using 65nm CMOS technology.

Afin de permettre le développement de modèles manipulables par les concepteurs, il est nécessaire de pouvoir comprendre le fonctionnement des nanotubes, en particulier le transport des électrons et leurs propriétés électroniques. C’est dans ce contexte général que cette thèse s’intègre. Le travail a été mené sur quatre plans : développement de modèles permettant la description des phénomènes physiques importants au niveau des dispositifs, expertise sur le fonctionnement des nano-composants permettant de dégager les ordres de grandeurs pertinents pour les dispositifs, les contraintes, la pertinence de quelques procédés de fabrication (reproductibilité, taux de défauts, collection de caractéristiques mesurées et développement éventuel d'expériences spécifiques, expertise et conception des circuits innovatifs pour l’électronique numérique avec ces nano-composants. / This PhD work presents a computationally efficient physics-based compact model for the Schottky barrier (SB) carbon nanotube field-effect transistor (CNTFET). This compact model includes a new analytical formulation of the channel charge, taking into account the influence of the source and drain SBs. Compact model simulation results (I–V characteristic and channel density of charge) as well as Monte Carlo simulation results, which are provided by a recent work, will be given and compared to each other and also to experimental data to validate the used approximations. Good agreement is observed over a large range of gate and drain biases. Furthermore, a scaling study is presented to examine the impact of technological parameters on the device figure of merit. Then, for the assessment of the SB on circuit performances, traditional logical circuits are designed using the SB-CNTFET compact model, and results are compared with a conventional CNTFET with zero-SB height. Finally, exploiting the particular properties of SB-CNTFETs, a three-valued static memory that is suitable for high density integration is presented.

The thesis deals with the design of digital I/O card with USB connectivity for Honeywell spol. s r.o. – HTS CZ o.z. company. The main objective is the delay elimination between reading the current state of the inputs and outputs setting which occurs on the actual used commercial card. Further initial analysis outlines possible solutions at the beginning of the work. One of chapters describes USB communication interface, including its com- munications protocol and USB driver implementation on the operating system Microsoft Windows. The digital card has four I/O ports consisting of eight lines. All ports have built-in protection against overcurrent and ESD protection. Digital isolator is used for USB. Power is supplied via USB, but it is optionally possible to connect an external power source. The last part of the thesis is focused on the card driver design.

User-programmable, integrated circuits (ICs) e.g. Field Programmable Gate Arrays (FPGAs) are increasingly popular for embedded, high-performance data exploitation. They combine the parallelization capability and processing power of application specific integrated circuits (ASICs) with the exibility, scalability and adaptability of software-based processing solutions. FPGAs provide powerful processing resources due to an optimal adaptation to the target application and a well-balanced ratio of performance, efficiency and parallelization. One drawback of FPGA-based data exploitation is the limited memory capacity of reconfigurable integrated circuits. Large-scale Digital Signal Processor (DSP) FPGAs provide approximately 4MB on-board random access memory (RAM) which is not sufficient to buffer the broadband sensor and result data. Hence, additional external memory is connected to the FPGA to increase on-board storage capacities. External memory devices like double data rate three synchronous dynamic random access memories (DDR3-SDRAM) provide very fast and wide bandwidth interfaces that represent a bottleneck when used in highly parallelized processing architectures. Independent processing modules are demanding concurrent read and write access. Within the master thesis, a concept for the integration of an external DDR3- SDRAM into an FPGA-based parallelized processing architecture is developed and implemented. The solution realizes time division multiple access (TDMA) to the external memory and virtual, low-latency memory extension to the on-board buffer capabilities. The integration of the external RAM does not change the way how on-board buffers are used (control, data-fow).

<div>Machine Learning (ML) workloads, being memory and compute-intensive, consume large amounts of power running on conventional computing systems, restricting their implementations to large-scale data centers. Thus, there is a need for building domain-specific hardware primitives for energy-efficient ML processing at the edge. One such approach is in-memory computing, which eliminates frequent and unnecessary data-transfers between the memory and the compute units, by directly computing the data where it is stored. Most of the chip area is consumed by on-chip SRAMs in both conventional von-Neumann systems (e.g. CPU/GPU) as well as application-specific ICs (e.g. TPU). Thus, we propose various circuit techniques to enable a range of computations such as bitwise Boolean and arithmetic computations, binary convolution operations, non-Boolean dot-product operations, lookup-table based computations, and spiking neural network implementation - all within standard SRAM memory arrays.</div><div><br></div><div>First, we propose X-SRAM, where, by using skewed sense amplifiers, bitwise Boolean operations such as NAND/NOR/XOR/IMP etc. can be enabled within 6T and 8T SRAM arrays. Moreover, exploiting the decoupled read/write ports in 8T SRAMs, we propose read-compute-store scheme where the computed data can directly be written back in the array simultaneously. </div><div><br></div><div>Second, we propose Xcel-RAM, where we show how binary convolutions can be enabled in 10T SRAM arrays for accelerating binary neural networks. We present charge sharing approach for performing XNOR operations followed by a population count (popcount) using both analog and digital techniques, highlighting the accuracy-energy tradeoff. </div><div><br></div><div>Third, we take this concept further and propose CASH-RAM, to accelerate non-Boolean operations, such as dot-products within standard 8T-SRAM arrays by utilizing the parasitic capacitances of bitlines and sourcelines. We analyze the non-idealities that arise due to analog computations and propose a self-compensation technique which reduces the effects of non-idealities, thereby reducing the errors. </div><div><br></div><div>Fourth, we propose ROM-embedded caches, RECache, using standard 8T SRAMs, useful for lookup-table (LUT) based computations. We show that just by adding an extra word-line (WL) or a source-line (SL), the same bit-cell can store a ROM bit, as well as the usual RAM bit, while maintaining the performance and area-efficiency, thereby doubling the memory density. Further we propose SPARE, an in-memory, distributed processing architecture built on RECache, for accelerating spiking neural networks (SNNs), which often require high-order polynomials and transcendental functions for solving complex neuro-synaptic models. </div><div><br></div><div>Finally, we propose IMPULSE, a 10T-SRAM compute-in-memory (CIM) macro, specifically designed for state-of-the-art SNN inference. The inherent dynamics of the neuron membrane potential in SNNs allows processing of sequential learning tasks, avoiding the complexity of recurrent neural networks. The highly-sparse spike-based computations in such spatio-temporal data can be leveraged for energy-efficiency. However, the membrane potential incurs additional memory access bottlenecks in current SNN hardware. IMPULSE triew to tackle the above challenges. It consists of a fused weight (WMEM) and membrane potential (VMEM) memory and inherently exploits sparsity in input spikes. We propose staggered data mapping and re-configurable peripherals for handling different bit-precision requirements of WMEM and VMEM, while supporting multiple neuron functionalities. The proposed macro was fabricated in 65nm CMOS technology. We demonstrate a sentiment classification task from the IMDB dataset of movie reviews and show that the SNN achieves competitive accuracy with only a fraction of trainable parameters and effective operations compared to an LSTM network.</div><div><br></div><div>These circuit explorations to embed computations in standard memory structures shows that on-chip SRAMs can do much more than just store data and can be re-purposed as on-demand accelerators for a variety of applications. </div>

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.