Characterizationof FD-SOI transistor

In this project, measurements have been made on FD-SOI transistors, fabricated by CEA-LETI, to carry out a characterization of these devices, since they are very new and need to be studied. This work has focused on characterizing the aging mechanism of the devices and the observed RTN. To characterize the aging mechanism and variability of the samples based on the applied cycles, the measurements have been made by applying constant stress voltages (CVS) directly to the device with a wafer prove station and a semiconductor parameter analyzer (SPA). To observe TN, different electrical procedures have been studied, controlling the different parameters during the measurements.En aquest projecte s'han realitzat mesures en transistors FD-SOI, fabricats per CEA-LETI, per tal de dur a terme una caracterització d'aquests dispositius, ja que són molt nous i necessiten de ser estudiats. Aquest treball s'ha centrat en caracteritzar l'envelliment dels dispositius i el RTN observat. Per a caracteritzar l'envelliment i la variabilitat de les mostres en funció dels cicles aplicats, les mesures s'han realitzat aplicant tensions d'estrés constant (CVS) directament al dispositiu amb una taula de puntes i un analitzador de paràmetres de semiconductors (SPA). Per tal d'observar RTN s'han estudiat diferents procediments elèctrics, controlant els diferents paràmetres durant les mesures.En este proyecto se han realizado medidas en transistores FD-SOI, fabricados por CEA-LETI, para llevar a cabo una caracterización de estos dispositivos, puesto que son muy nuevos y necesitan de ser estudiados. Este trabajo se ha centrado en caracterizar los mecanismos de envejecimiento de los dispositivos y el RTN observado. Para caracterizar el envejecimiento y la variabilidad de las muestras en función de los ciclos aplicados, las medidas se han realizado aplicando tensiones de estrés constante (CVS) directamente al dispositivo con una tabla de puntas y un analizador de parámetros de semiconductores (SPA). Para observar RTN se han estudiado diferentes procedimientos eléctricos, controlando los diferentes parámetros durante las medidas

Scaling and variability in ultra thin body silicon on insulator (UTB SOI) MOSFETs

The main objective of this thesis is to perform a comprehensive simulation study of the statistical variability in well scaled fully depleted ultra thin body silicon on insulator (FD-UTB SOI) at nanometer regime. It describes the design procedure for template FDUTB SOI transistor scaling and the impacts of statistical variability and reliability the scaled template transistor. The starting point of this study is a systematic simulation analysis based on a welldesigned 32nm thin body SOI template transistor provided by the FP7 project PULLNANO. The 32nm template transistor is consistent with the International Technology Roadmap for Semiconductor (ITRS) 2009 specifications. The wellestablished 3D ‘atomistic’ simulator GARAND has been employed in the designing of the scaled transistors and to carry out the statistical variability simulations. Following the foundation work in characterizing and optimizing the template 32 nm gate length transistor, the scaling proceeds down to 22 nm, 16 nm and 11 nm gate lengths using typically 0.7 scaling factor in respect of the horizontal and vertical transistor dimensions. The device design process is targeted for low power applications with a careful consideration of the impacts of the design parameters choice including buried oxide thickness (TBOX), source/drain doping abruptness (σ) and spacer length (Lspa). In order to determine the values of TBOX, σ, and Lspa, it is important to analyze simulation results, carefully assessing the impact on manufacturability and to consider the corresponding trade-off between short channel effects and on-current performance. Considering the above factors, TBOX = 10nm, σ = 2nm/dec and Lspa = 7nm have been adopted as optimum values respectively. iv The statistical variability of the transistor characteristics due to intrinsic parameter fluctuation (IPF) in well-scaled FD-UTB SOI devices is systematically studied for the first time. The impact of random dopant fluctuation (RDF), line edge roughness (LER) and metal gate granularity (MGG) on threshold voltage (Vth), on-current (Ion) and drain induced barrier lowering (DIBL) are analysed. Each principal sources of variability is treated individually and in combination with other variability sources in the simulation of large ensembles of microscopically different devices. The introduction of highk/ metal gate stack has improved the electrostatic integrity and enhanced the overall device performance. However, in the case of fully depleted channel transistors, MGG has become a dominant variability factor for all critical electrical parameters at gate first technology. For instance, σVth due to MGG increased to 41.9 mV at 11nm gate length compared to 26.0 mV at 22nm gate length. Similar trend has also been observed in σIon, increasing from 0.065 up to 0.174 mA/μm when the gate length is reduced from 22 nm down to 11 nm. Both RDF and LER have significant role in the intrinsic parameter fluctuations and therefore, none of these sources should be overlooked in the simulations. Finally, the impact of different variability sources in combination with positive bias temperature instability (PBTI) degradation on Vth, Ion and DIBL of the scaled nMOSFETs is investigated. Our study indicates that BTI induced charge trapping is a crucial reliability problem for the FD-UTB SOI transistors operation. Its impact not only introduces a significant degradation of transistor performance, but also accelerates the statistical variability. For example, the effect of a late degradation stage (at trap density of 1e12/cm2) in the presence of RDF, LER and MGG results in σVth increase to 36.9 mV, 45.0 mV and 58.3 mV for 22 nm, 16 nm and 11 nm respectively from the original 29.0 mV, 37.9 mV and 50.4 mV values in the fresh transistors

Impact of Random Telegraph Signals on 6T high-density SRAM in 28nm UTBB FD-SOI

session A7L-E: Advanced CMOSInternational audienceThis work investigates the impact of Random Telegraph Signal (RTS) noise on a 6 Transistors single P-well Static Random Access Memory (6T-SRAM) manufactured in 28nm Ultra-Thin Body and Buried Oxide Fully-Depleted Silicon-On-Insulator (UTBB FD-SOI) technology. A SPICE-level bias-and time-dependent RTS model peculiar to UTBB FD-SOI, which considers both front- and back-gate of the device as RTS sources, is presented. The Bit-Error-Rate is evaluated on silicon dies through the write-ability (WA) failure criterion and with a dedicated back-biasing strategy. Simulations evidence the role of RTS-induced dynamic variability with respect to process variability and show a good agreement with measurements

Hardware / Software Architectural and Technological Exploration for Energy-Efficient and Reliable Biomedical Devices

Nowadays, the ubiquity of smart appliances in our everyday lives is increasingly strengthening the links between humans and machines. Beyond making our lives easier and more convenient, smart devices are now playing an important role in personalized healthcare delivery. This technological breakthrough is particularly relevant in a world where population aging and unhealthy habits have made non-communicable diseases the first leading cause of death worldwide according to international public health organizations. In this context, smart health monitoring systems termed Wireless Body Sensor Nodes (WBSNs), represent a paradigm shift in the healthcare landscape by greatly lowering the cost of long-term monitoring of chronic diseases, as well as improving patients' lifestyles. WBSNs are able to autonomously acquire biological signals and embed on-node Digital Signal Processing (DSP) capabilities to deliver clinically-accurate health diagnoses in real-time, even outside of a hospital environment. Energy efficiency and reliability are fundamental requirements for WBSNs, since they must operate for extended periods of time, while relying on compact batteries. These constraints, in turn, impose carefully designed hardware and software architectures for hosting the execution of complex biomedical applications. In this thesis, I develop and explore novel solutions at the architectural and technological level of the integrated circuit design domain, to enhance the energy efficiency and reliability of current WBSNs. Firstly, following a top-down approach driven by the characteristics of biomedical algorithms, I perform an architectural exploration of a heterogeneous and reconfigurable computing platform devoted to bio-signal analysis. By interfacing a shared Coarse-Grained Reconfigurable Array (CGRA) accelerator, this domain-specific platform can achieve higher performance and energy savings, beyond the capabilities offered by a baseline multi-processor system. More precisely, I propose three CGRA architectures, each contributing differently to the maximization of the application parallelization. The proposed Single, Multi and Interleaved-Datapath CGRA designs allow the developed platform to achieve substantial energy savings of up to 37%, when executing complex biomedical applications, with respect to a multi-core-only platform. Secondly, I investigate how the modeling of technology reliability issues in logic and memory components can be exploited to adequately adjust the frequency and supply voltage of a circuit, with the aim of optimizing its computing performance and energy efficiency. To this end, I propose a novel framework for workload-dependent Bias Temperature Instability (BTI) impact analysis on biomedical application results quality. Remarkably, the framework is able to determine the range of safe circuit operating frequencies without introducing worst-case guard bands. Experiments highlight the possibility to safely raise the frequency up to 101% above the maximum obtained with the classical static timing analysis. Finally, through the study of several well-known biomedical algorithms, I propose an approach allowing energy savings by dynamically and unequally protecting an under-powered data memory in a new way compared to regular error protection schemes. This solution relies on the Dynamic eRror compEnsation And Masking (DREAM) technique that reduces by approximately 21% the energy consumed by traditional error correction codes