Statistical leakage estimation in 32nm CMOS considering cells correlations

International audienceIn this paper a method to estimate the leakage power consumption of CMOS digital circuits taking into account input states and process variations is proposed. The statistical leakage estimation is based on a pre-characterization of library cells considering correlations (ρ) between cells leakages. A method to create cells leakage correlation matrix is introduced. The maximum relative error achieved in the correlation matrix is 0.4% with respect to the correlations obtained by Monte Carlo simulations. Next the total circuit leakage is calculated from this matrix and cells leakage means and variances. The accuracy and efficiency of the approach is demonstrated on a C3540 (8 bit ALU) ISCAS85 Benchmark circuit

Parametric Yield of VLSI Systems under Variability: Analysis and Design Solutions

Variability has become one of the vital challenges that the designers of integrated circuits encounter. variability becomes increasingly important. Imperfect manufacturing process manifest itself as variations in the design parameters. These variations and those in the operating environment of VLSI circuits result in unexpected changes in the timing, power, and reliability of the circuits. With scaling transistor dimensions, process and environmental variations become significantly important in the modern VLSI design. A smaller feature size means that the physical characteristics of a device are more prone to these unaccounted-for changes. To achieve a robust design, the random and systematic fluctuations in the manufacturing process and the variations in the environmental parameters should be analyzed and the impact on the parametric yield should be addressed. This thesis studies the challenges and comprises solutions for designing robust VLSI systems in the presence of variations. Initially, to get some insight into the system design under variability, the parametric yield is examined for a small circuit. Understanding the impact of variations on the yield at the circuit level is vital to accurately estimate and optimize the yield at the system granularity. Motivated by the observations and results, found at the circuit level, statistical analyses are performed, and solutions are proposed, at the system level of abstraction, to reduce the impact of the variations and increase the parametric yield. At the circuit level, the impact of the supply and threshold voltage variations on the parametric yield is discussed. Here, a design centering methodology is proposed to maximize the parametric yield and optimize the power-performance trade-off under variations. In addition, the scaling trend in the yield loss is studied. Also, some considerations for design centering in the current and future CMOS technologies are explored. The investigation, at the circuit level, suggests that the operating temperature significantly affects the parametric yield. In addition, the yield is very sensitive to the magnitude of the variations in supply and threshold voltage. Therefore, the spatial variations in process and environmental variations make it necessary to analyze the yield at a higher granularity. Here, temperature and voltage variations are mapped across the chip to accurately estimate the yield loss at the system level. At the system level, initially the impact of process-induced temperature variations on the power grid design is analyzed. Also, an efficient verification method is provided that ensures the robustness of the power grid in the presence of variations. Then, a statistical analysis of the timing yield is conducted, by taking into account both the process and environmental variations. By considering the statistical profile of the temperature and supply voltage, the process variations are mapped to the delay variations across a die. This ensures an accurate estimation of the timing yield. In addition, a method is proposed to accurately estimate the power yield considering process-induced temperature and supply voltage variations. This helps check the robustness of the circuits early in the design process. Lastly, design solutions are presented to reduce the power consumption and increase the timing yield under the variations. In the first solution, a guideline for floorplaning optimization in the presence of temperature variations is offered. Non-uniformity in the thermal profiles of integrated circuits is an issue that impacts the parametric yield and threatens chip reliability. Therefore, the correlation between the total power consumption and the temperature variations across a chip is examined. As a result, floorplanning guidelines are proposed that uses the correlation to efficiently optimize the chip's total power and takes into account the thermal uniformity. The second design solution provides an optimization methodology for assigning the power supply pads across the chip for maximizing the timing yield. A mixed-integer nonlinear programming (MINLP) optimization problem, subject to voltage drop and current constraint, is efficiently solved to find the optimum number and location of the pads

CAD Techniques for Robust FPGA Design Under Variability

The imperfections in the semiconductor fabrication process and uncertainty in operating environment of VLSI circuits have emerged as critical challenges for the semiconductor industry. These are generally termed as process and environment variations, which lead to uncertainty in performance and unreliable operation of the circuits. These problems have been further aggravated in scaled nanometer technologies due to increased process variations and reduced operating voltage. Several techniques have been proposed recently for designing digital VLSI circuits under variability. However, most of them have targeted ASICs and custom designs. The flexibility of reconfiguration and unknown end application in FPGAs make design under variability different for FPGAs compared to ASICs and custom designs, and the techniques proposed for ASICs and custom designs cannot be directly applied to FPGAs. An important design consideration is to minimize the modifications in architecture and circuit to reduce the cost of changing the existing FPGA architecture and circuit. The focus of this work can be divided into three principal categories, which are, improving timing yield under process variations, improving power yield under process variations and improving the voltage profile in the FPGA power grid. The work on timing yield improvement proposes routing architecture enhancements along with CAD techniques to improve the timing yield of FPGA designs. The work on power yield improvement for FPGAs selects a low power dual-Vdd FPGA design as the baseline FPGA architecture for developing power yield enhancement techniques. It proposes CAD techniques to improve the power yield of FPGAs. A mathematical programming technique is proposed to determine the parameters of the buffers in the interconnect such as the sizes of the transistors and threshold voltage of the transistors, all within constraints, such that the leakage variability is minimized under delay constraints. Two CAD techniques are investigated and proposed to improve the supply voltage profile of the power grids in FPGAs. The first technique is a place and route technique and the second technique is a logic clustering technique to reduce IR-drops and spatial variation of supply voltage in the power grid

Statistical Yield Analysis and Design for Nanometer VLSI

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

Resilient Design for Process and Runtime Variations

The main objective of this thesis is to tackle the impact of parameter variations in order to improve the chip performance and extend its lifetime

Algorithms and methodologies for interconnect reliability analysis of integrated circuits

The phenomenal progress of computing devices has been largely made possible by the sustained efforts of semiconductor industry in innovating techniques for extremely large-scale integration. Indeed, gigantically integrated circuits today contain multi-billion interconnects which enable the transistors to talk to each other -all in a space of few mm2. Such aggressively downscaled components (transistors and interconnects) silently suffer from increasing electric fields and impurities/defects during manufacturing. Compounded by the Gigahertz switching, the challenges of reliability and design integrity remains very much alive for chip designers, with Electro migration (EM) being the foremost interconnect reliability challenge. Traditionally, EM containment revolves around EM guidelines, generated at single-component level, whose non-compliance means that the component fails. Failure usually refers to deformation due to EM -manifested in form of resistance increase, which is unacceptable from circuit performance point of view. Subsequent aspects deal with correct-by-construct design of the chip followed by the signoff-verification of EM reliability. Interestingly, chip designs today have reached a dilemma point of reduced margin between the actual and reliably allowed current densities, versus, comparatively scarce system-failures. Consequently, this research is focused on improved algorithms and methodologies for interconnect reliability analysis enabling accurate and design-specific interpretation of EM events. In the first part, we present a new methodology for logic-IP (cell) internal EM verification: an inadequately attended area in the literature. Our SPICE-correlated model helps in evaluating the cell lifetime under any arbitrary reliability speciation, without generating additional data - unlike the traditional approaches. The model is apt for today's fab less eco-system, where there is a) increasing reuse of standard cells optimized for one market condition to another (e.g., wireless to automotive), as well as b) increasing 3rd party content on the chip requiring a rigorous sign-off. We present results from a 28nm production setup, demonstrating significant violations relaxation and flexibility to allow runtime level reliability retargeting. Subsequently, we focus on an important aspect of connecting the individual component-level failures to that of the system failure. We note that existing EM methodologies are based on serial reliability assumption, which deems the entire system to fail as soon as the first component in the system fails. With a highly redundant circuit topology, that of a clock grid, in perspective, we present algorithms for EM assessment, which allow us to incorporate and quantify the benefit from system redundancies. With the skew metric of clock-grid as a failure criterion, we demonstrate that unless such incorporations are done, chip lifetimes are underestimated by over 2x. This component-to-system reliability bridge is further extended through an extreme order statistics based approach, wherein, we demonstrate that system failures can be approximated by an asymptotic kth-component failure model, otherwise requiring costly Monte Carlo simulations. Using such approach, we can efficiently predict a system-criterion based time to failure within existing EDA frameworks. The last part of the research is related to incorporating the impact of global/local process variation on current densities as well as fundamental physical factors on EM. Through Hermite polynomial chaos based approach, we arrive at novel variations-aware current density models, which demonstrate significant margins (> 30 %) in EM lifetime when compared with the traditional worst case approach. The above research problems have been motivated by the decade-long work experience of the author dealing with reliability issues in industrial SoCs, first at Texas Instruments and later at Qualcomm.L'espectacular progrés dels dispositius de càlcul ha estat possible en gran part als esforços de la indústria dels semiconductors en proposar tècniques innovadores per circuits d'una alta escala d'integració. Els circuits integrats contenen milers de milions d'interconnexions que permeten connectar transistors dins d'un espai de pocs mm2. Tots aquests components estan afectats per camps elèctrics, impureses i defectes durant la seva fabricació. Degut a l’activitat a nivell de Gigahertzs, la fiabilitat i integritat són reptes importants pels dissenyadors de xips, on la Electromigració (EM) és un dels problemes més importants. Tradicionalment, el control de la EM ha girat entorn a directrius a nivell de component. L'incompliment d’alguna de les directrius implica un alt risc de falla. Per falla s'entén la degradació deguda a la EM, que es manifesta en forma d'augment de la resistència, la qual cosa és inacceptable des del punt de vista del rendiment del circuit. Altres aspectes tenen a veure amb la correcta construcció del xip i la verificació de fiabilitat abans d’enviar el xip a fabricar. Avui en dia, el disseny s’enfronta a dilemes importants a l’hora de definir els marges de fiabilitat dels xips. És un compromís entre eficiència i fiabilitat. La recerca en aquesta tesi se centra en la proposta d’algorismes i metodologies per a l'anàlisi de la fiabilitat d'interconnexió que permeten una interpretació precisa i específica d'esdeveniments d'EM. A la primera part de la tesi es presenta una nova metodologia pel disseny correcte-per-construcció i verificació d’EM a l’interior de les cel·les lògiques. Es presenta un model SPICE correlat que ajuda a avaluar el temps de vida de les cel·les segons qualsevol especificació arbitrària de fiabilitat i sense generar cap dada addicional, al contrari del que fan altres tècniques. El model és apte per l'ecosistema d'empreses de disseny quan hi ha a) una reutilització creixent de cel·les estàndard optimitzades per unes condicions de mercat i utilitzades en un altre (p.ex. de wireless a automoció), o b) la utilització de components del xip provinents de terceres parts i que necessiten una verificació rigorosa. Es presenten resultats en una tecnologia de 28nm, demostrant relaxacions significatives de les regles de fiabilitat i flexibilitat per permetre la reavaluació de la fiabilitat en temps d'execució. A continuació, el treball tracta un aspecte important sobre la relació entre les falles dels components i les falles del sistema. S'observa que les tècniques existents es basen en la suposició de fiabilitat en sèrie, que porta el sistema a fallar tant aviat hi ha un component que falla. Pensant en topologies redundants, com la de les graelles de rellotge, es proposen algorismes per l'anàlisi d'EM que permeten quantificar els beneficis de la redundància en el sistema. Utilitzant com a mètrica l’esbiaixi del senyal de rellotge, es demostra que la vida dels xips pot arribar a ser infravalorada per un factor de 2x. Aquest pont de fiabilitat entre component i sistema es perfecciona a través d'una tècnica basada en estadístics d'ordre extrem on es demostra que les falles poden ser aproximades amb un model asimptòtic de fallada de l'ièssim component, evitant així simulacions de Monte Carlo costoses. Amb aquesta tècnica, es pot predir eficientment el temps de fallada a nivell de sistema utilitzant eines industrials. La darrera part de la recerca està relacionada amb avaluar l'impacte de les variacions de procés en les densitats de corrent i factors físics de la EM. Mitjançant una tècnica basada en polinomis d'Hermite s'han obtingut uns nous models de densitat de corrent que mostren millores importants (>30%) en l'estimació de la vida del sistema comprades amb les tècniques basades en el cas pitjor. La recerca d'aquesta tesi ha estat motivada pel treball de l'autor durant més d'una dècada tractant temes de fiabilitat en sistemes, primer a Texas Instruments i després a Qualcomm.Postprint (published version

Reliability and security in low power circuits and systems

With the massive deployment of mobile devices in sensitive areas such as healthcare and defense, hardware reliability and security have become hot research topics in recent years. These topics, although different in definition, are usually correlated. This dissertation offers an in-depth treatment on enhancing the reliability and security of low power circuits and systems. The first part of the dissertation deals with the reliability of sub-threshold designs, which use supply voltage lower than the threshold voltage (Vth) of transistors to reduce power. The exponential relationship between delay and Vth significantly jeopardizes their reliability due to process variation induced timing violations. In order to address this problem, this dissertation proposes a novel selective body biasing scheme. In the first work, the selective body biasing problem is formulated as a linearly constrained statistical optimization model, and the adaptive filtering concept is borrowed from the signal processing community to develop an efficient solution. However, since the adaptive filtering algorithm lacks theoretical justification and guaranteed convergence rate, in the second work, a new approach based on semi-infinite programming with incremental hypercubic sampling is proposed, which demonstrates better solution quality with shorter runtime. The second work deals with the security of low power crypto-processors, equipped with Random Dynamic Voltage Scaling (RDVS), in the presence of Correlation Power Analysis (CPA) attacks. This dissertation firstly demonstrates that the resistance of RDVS to CPA can be undermined by lowering power supply voltage. Then, an alarm circuit is proposed to resist this attack. However, the alarm circuit will lead to potential denial-of-service due to noise-triggered false alarms. A non-zero sum game model is then formulated and the Nash Equilibria is analyzed --Abstract, page iii

Centralized modeling of the communication space for spectral awareness in cognitive radio networks

The communication space is five dimensional: its degrees of freedom are frequency, time and space. The use of the electromagnetic spectrum depends on these parameters. With future applications such as opportunistic overlay access or distributed spectrum monitoring in mind, it is important to estimate the state of the communication space on the basis of incomplete or imprecise information. A promising approach are technology centric Cognitive Radio networks. In these networks, nodes cooperate to infer information on spectral occupancy. This conceptual paper proposes a novel approach for centralized modeling of the communication space with emphasis on spatial dependencies through the use of a regression model. The modeling approach is verified with practical measurements

Methodologies to detect leakages from geological carbon storage sites

2014 Summer.Includes bibliographical references.Geological carbon storage (GCS) has been proposed as a favorable technology to reduce carbon dioxide (CO2) emissions to the atmosphere. Candidate storage formations include abandoned oil and natural gas reservoirs, un-mineable coal seams, and deep saline aquifers. The large global storage capacity and widespread occurrence of deep saline formations make them ideal repositories of large volumes of CO2, however they generally lack of data for geological characterization in comparison to oil and gas reservoirs. Thus, properties of the injected formation or the sealing formation are unknown, which implies that the evolution and movement of the CO2 plume are uncertain in these geological formations. The first part of this research aims to provide an understanding of the main sources of uncertainty during the injection of CO2 that cause leakage variability and fluid pressure change near the injection well, which could be responsible for fracturing the sealing formation. With this purpose the effect of uncertain parameters such as permeability and porosity of injected aquifer, permeability of CO2 leakage pathways through the sealing layers, system compressibility, and brine residual saturation are investigated using stochastic and global sensitivity analyses. These analyses are applied to a potential candidate site for GCS located at the Michigan Basin. Results show aquifer permeability and system compressibility are the most influential parameters on fluid overpressure and CO2 mass leakage. Other parameters, such as rock porosity, permeability of passive wells, and brine residual saturation do not influence fluid overpressure nearby the injection well. CO2 mass leakage is found to be sensitive to passive well permeability as well as the type of statistical distribution applied to describe well permeability. Scarce data of the Michigan Basin exist that can be used directly to describe the spatial distribution at the basin scale of the caprock overlying the candidate site. The continuity of this formation is uncertain. The second part of this investigation explores the application of binary permeability fields for the study of CO2 leakage from GCS at the candidate site. A sequential indicator simulation algorithm is used to populate binary permeability fields representing a caprock formation with potential leaky areas (or inclusions). Results of the caprock continuity uncertainty conclude that increasing the probability of inclusions occurrence increases the CO2 leakage. In addition, the correlation length used by the sequential indicator simulator affects the occurrence of inclusions. The third part investigates the detection and location of the presence of possible brine or carbon leakage pathways at the caprock during the injection operations of a GCS system. A computational framework for the assimilation of changes in head pressure data into a subsurface flow model is created to study the evolution of the CO2 plume and brine movement. The capabilities of two data assimilation algorithms, the ensemble smoother (ES) and the ensemble Kalman smoother (EnKS), to identify and locate the leaky pathways are examined. Results suggest that the EnKS is more effective than the ES in characterizing caprock discontinuities