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

PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications

In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be largely employed in Wide Area Monitoring, Protection and Control (WAMPAC) applications. However, a standard approach towards ROCOF measurements is still missing. In this paper, we investigate the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS). For this analysis, we select three state-of-the-art window-based synchrophasor estimation algorithms and compare different signal models, ROCOF estimation techniques and window lengths in datasets inspired by real-world acquisitions. In this sense, we are able to carry out a sensitivity analysis of the behavior of a PMU-based UFLS control scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters, as long as the harmonic and inter-harmonic distortion within the measurement pass-bandwidth is scarce. In the presence of transient events, the synchrophasor model looses its appropriateness as the signal energy spreads over the entire spectrum and cannot be approximated as a sequence of narrow-band components. Finally, we validate the actual feasibility of PMU-based UFLS in a real-time simulated scenario where we compare two different ROCOF estimation techniques with a frequency-based control scheme and we show their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE Transactions on Instrumentation and Measurement (acceptance date: 9 March 2019

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

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

DFM Techniques for the Detection and Mitigation of Hotspots in Nanometer Technology

With the continuous scaling down of dimensions in advanced technology nodes, process variations are getting worse for each new node. Process variations have a large influence on the quality and yield of the designed and manufactured circuits. There is a growing need for fast and efficient techniques to characterize and mitigate the effects of different sources of process variations on the design's performance and yield. In this thesis we have studied the various sources of systematic process variations and their effects on the circuit, and the various methodologies to combat systematic process variation in the design space. We developed abstract and accurate process variability models, that would model systematic intra-die variations. The models convert the variation in process into variation in electrical parameters of devices and hence variation in circuit performance (timing and leakage) without the need for circuit simulation. And as the analysis and mitigation techniques are studied in different levels of the design ow, we proposed a flow for combating the systematic process variation in nano-meter CMOS technology. By calculating the effects of variability on the electrical performance of circuits we can gauge the importance of the accurate analysis and model-driven corrections. We presented an automated framework that allows the integration of circuit analysis with process variability modeling to optimize the computer intense process simulation steps and optimize the usage of variation mitigation techniques. And we used the results obtained from using this framework to develop a relation between layout regularity and resilience of the devices to process variation. We used these findings to develop a novel technique for fast detection of critical failures (hotspots) resulting from process variation. We showed that our approach is superior to other published techniques in both accuracy and predictability. Finally, we presented an automated method for fixing the lithography hotspots. Our method showed success rate of 99% in fixing hotspots

Advanced Technologies in Hydropower Flow Systems

Hydropower is an essential part of the renewable energy sector. High efficiency, immediate availability, and safe operation of hydroelectric power plants are the three key issues in recent developments in the hydropower sector. This book brings together the latest achievements addressing these key factors. In addition, one contribution deals with the alternative harvesting of hydro energy from pivoted cylinders by generating flow-induced vibrations, which are unwanted phenomena in classical pump–turbine units

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