Managing Variability in VLSI Circuits.

Over the last two decades, Design for Manufacturing (DFM) has emerged as an essential field within the semiconductor industry. The main objective of DFM is to reduce and, if possible, eliminate variability in integrated circuits (ICs). Numerous techniques for managing variation have emerged throughout IC design: manufacturers design instruments with minute tolerances, process engineers calibrate and characterize a given process throughout its lifetime, and IC designers strive to model and characterize variability within their devices, libraries, and circuits. This dissertation focuses on the last of these three techniques and presents material relevant to managing variability within IC design. Since characterization and modeling are essential to the analysis and reduction of variation in modern-day designs, this dissertation begins by studying various correlation models used within Statistical Static Timing Analysis (SSTA). In the end, the study shows that using complex correlation models does not necessarily result in significant error reduction within SSTA, and that simple models (which only include die-to-die and random variation) can therefore be used to achieve similar accuracy with reduced overhead and run-time. Next, the variation models, themselves, are explored and a new critical dimension (CD) model is proposed which reduces standard deviation error in SSTA by ~3X. Finally, the focus changes from the timing analysis level and moves lower in the design hierarchy to the libraries and devices that comprise the backbone of IC design. The final three chapters study mechanical stress enhancement and discuss how to fully exploit the layout dependencies of mechanically stressed silicon. The first of these three chapters presents an optimization scheme that uses the layout dependencies of stress in conjunction with dual-threshold-voltage (Vth) assignment to decrease leakage power consumption by ~24%. Next, the second of the three chapters proposes a new standard cell library design methodology, called “STEEL.” STEEL provides average delay improvements of 11% over equivalent single-Vth implementations, while consuming 2.5X less leakage than the dual-Vth alternative. Finally, the stress enhanced studies (and this document) are concluded by a new optimization scheme that combines stress enhancement with gate length biasing to achieve 2.9X leakage power savings in IC designs without modifying Vth.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75947/1/btcline_1.pd

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

Hydroclimatic Variability and Predictability: A Survey of Recent Research

Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i) variability in general, (ii) droughts, (iii) floods, (iv) land-atmosphere coupling, and (v) hydroclimatic prediction. Each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Taken together, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted

Remote Sensing of Hydro-Meteorology

Flood/drought, risk management, and policy: decision-making under uncertainty. Hydrometeorological extremes and their impact on human–environment systems. Regional and nonstationary frequency analysis of extreme events. Detection and prediction of hydrometeorological extremes with observational and model-based approaches. Vulnerability and impact assessment for adaptation to climate change

Global Food Security Under Climate-Water-Energy Nexus

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water and energy crisis, and extreme weather, climate change, etc. threaten the global food security. The focus of this study is to advance the understanding of the associations between crops productivity and large scale and local scale climatic variables, climate change, technology enhancement and assessing food-water-energy nexus to enhance global food security in a sustainable manner considering current available resources. As decision-makers try to improve food security, it is important to identify the impact of technology enhancement and major climatic patterns that impact crop yields, quantify and predict their impacts and find the trade strategies to minimize current and potential food shortages. Annually crop yields of the global countries are impacted by climatic variables deferentially in magnitude and sign. Some of these climate variables triggers simultaneous impacts on crops that can cause synergistic crops variability and volatility across the globe. Considering these climatic patterns as well as other factors such as population distribution, available freshwater, cropland and energy resources, a global optimization model can enhance global food security through maximizing crop production. In this study we use historical records of crops yield, production, cropland area, international crops trade data, a broad range of climatic and non-climatic data, energy and freshwater resources. We implement data mining, statistical analysis, predictive and optimization modeling tools to shed light on the food security topic. This study will be performed at the global scale to the extent that data coverage allows. A global exploration of food security provides a broader understanding of this issue, and depending on the implemented methodology, may inform us about space-time interconnections of the desired variables. Diagnosing potential predictability of global crop yields in the near term is of utmost importance for ensuring food supply and preventing socio-economic consequences. While agricultural influence of climate is well-established, a detailed account of the characteristics of synergistic multi-national variability and world-wide volatility of crop yields, whereby many countries undergo harmonizing influences of climate to thwart or facilitate crops productivity, remains largely unexplored. History indicates that such synchronous volatility-led crop yields losses can leave major ramification for global price and food security. Previous studies suggest that a substantial proportion of global yields depends on local climate and larger-scale ocean atmospheric patterns. It is however unclear whether synergistic variability and volatility (major departure from the normal) of multi-national crop yields can be potentially predicted by larger-scale climate drivers. Using observed data of yields and climate variability from 1961-2013, we diagnose that yields of 5 staple crops, namely maize, rice, sorghum, soybean and wheat vary synergistically across key producing nations and can also be concurrently volatile, as a function of shared larger-scale climate drivers. We use a statistical approach called Robust Principal Component Analysis, to decouple and quantify the leading modes of global yield variability. Sea surface temperature anomalies, multiple atmospheric and oceanic indices, air temperature anomalies and Palmer Drought Severity Index are used to study the association between yields variability/volatility and climate. Results show that large-scale climate, especially El Niño-Southern Oscillation and North Atlantic Oscillation are strongly correlated with persistent and anomalous yield variability. The impact of local climate variability in both concurrent and lag phases vary among different countries. In addition to extreme wet conditions across sorghum croplands in South America, extensive significant hot or drought patterns are recognized across maize croplands of South America and south of Asia, rice harvesting regions of Oceania and south of Asia and sorghum and soybean growing regions of North America, south and southeast of Asia. Results show that warmer-than-normal winter time sea surface temperature anomalies in the Pacific Ocean exerts the most dominating influence on global rice and sorghum yield volatility. In addition, extreme soybean and maize volatility are associated with mutual climatic teleconnection patterns. We diagnose that wheat yields can be concurrently volatile, as a function of shared larger-scale climate drivers. Results also demonstrate that world-wide wheat yield volatility has become more common in the current most decades, associating with warmer northern Pacific and Atlantic oceans, negative North Atlantic Oscillation, negative Scandinavian Pattern, and positive Southern Annular Mode, leading mostly to global wheat supply shortage. We found out not only do the same crops in many countries co-vary significantly, but different crops co-vary in a same/different manner. Then we present a predictive model of the changes in the crop yields and how they relate to different large-scale and regional climate and climate change variables and technology in a unified framework. A new Bayesian multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño- Southern Oscillation, Palmer Drought Severity Index, geopotential height anomalies, historical carbon dioxide concentration and country-based time series of Gross Domestic Product per capita -as an approximation of technology measurement- are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. We found out these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of sample verifications. While some countries were not generally affected by climatic factors, Palmer Drought Severity Index and geopotential height anomalies acted both positively and negatively in different regions for crop yields in many countries. In the next step we assess approaches to maximize total production of barley, maize, rice, sorghum, soybean and wheat across the global countries. The model is tested based on the current available freshwater resources and croplands area as well as a energy constraint. The results show that total production of these crops in many countries can be increased substantially. This analysis will provide the essential scientific motivation at the national and global scale to discover feasible regions and different crop choices that can support the sustainable area expansion and crop production enhancement. The results of this research try to improve three pillars of food security namely availability, access, and stability. Our work tries to enhance the knowledge of global food security field, which is of relevance to policy initiatives, decision makers, water and energy managers, government and non-government organizations such as United States Department of Agriculture, Food and Agricultural Organization of United Nations, stakeholders, insurance companies and scientists with similar interests to ours

Evaluating climate models with the CLIVAR 2020 ENSO Metrics Package

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged

Moisture and Thermal Characteristics of Southern Plains Ice Storms: Insights from a regional climatology and high-resolution WRF-ARW sensitivity study

Winter storms, including snowstorms and ice storms, are infrequent in the Southern Great Plains of the United States (SGP), but can produce significant hazard and socioeconomic disruption. During 2000-2010, several severe ice storms impacted the region. These events combined resulted in nearly $800 million in damages, over 30 fatalities, and power disruption to over 3 million homes and businesses. Hitherto, basic climatological information for winter storms in this region remain understudied. This dissertation examines the characteristics of freezing precipitation events for the SGP by developing a regional spatial and synoptic climatology (1993-2011). Thermal profiles conducive to winter precipitation of varying types and intensities are also examined and compared with past literature. A combination of sounding analysis, and Principal Component (PC)/composite techniques are used to derive this climatology. Results identified that the SGP experiences freezing precipitation of varying intensity, but that ice storms to the region are notable for their large above-freezing inversion layer (‘warm layer’) temperatures/depths and mixing ratio. Freezing precipitation occurs most often over the central and eastern domain during December-February, while snowfall maximizes northwest of this zone with broader seasonal occurrence. The synoptic analysis showed that patterns conductive to storms with a pronounced mixed-phase region typically involved topographically aided ageostropic down-gradient advection of cold stable air in the lee of the Rocky Mountains, with an arctic high pressure over the northern/central Great Plains. A mid-level trough and low-level warm air advection provided ascent, and anomalously warm air to the south provided sufficient support for a warm layer. Long-duration ice storms were observed with a slow-moving high-amplitude western trough, direct moisture transport from the Gulf of Mexico, and a ridge over the southeastern U.S. Based on the climatology and past literature, a hypothesis is proposed that the Gulf of Mexico, as the proximal basin and major moisture source, may impact ice storm severity by modulation of the warm layer profile associated with strongly positive or negative SST anomalies. This hypothesis is tested using high-resolution nested WRF-ARW sensitivity studies with six representations of SST, including the 30-year climatology, a uniform ±2 degrees K perturbation to the control, and a physical upper and lower limit using the SST field for the warmest and coolest basin-average anomalies 1981-2011. Two case studies were utilized corresponding to different synoptic types. The simulations revealed discernible influence of SST on freezing precipitation, including its temporal evolution and intensity. For the December 9-11 2007 case study, the warm layer formed well prior to the event, associated with persistent southerly flow and a warm anomaly over the southern U.S. The impact of SST on the warm layer intensity was weak in comparison to its existing magnitude, however the atmospheric stability profile was altered such that strongly negative SST produced stabilization above the maximum inversion temperature and markedly reduced precipitation on the first day of the ice storm. A dynamical weakening of the low-level jet and moisture transport in the strongly positive SST case counteracted observed increases in mixing ratio to yield weaker accumulation differences during the second precipitation episode. For the January 28-30 2010 case study, the impact of SST was more pronounced on the warm layer, which had formed in association with return flow from the Gulf. Warmer SST, especially strongly positive localized anomalies within the fetch of the impacted area, lead to both a moisture induced intensification of precipitation, and increased peak warm layer temperature, leading to changes in the location of freezing precipitation versus rain/snow, especially for Arkansas. Dynamical intensification (weakening) of precipitation occurred as increased (decreased) baroclinicity, warm air advection and latent heat release promoted a stronger geopotential low at 850 hPa, and a strengthened (weakened) low-level jet yielding greater (less) moisture transport. Despite the differing thermal and dynamical responses, both case studies displayed potential for enhanced icing conditions with warmer SST, while cooler SST produced a marked reduction in severity. The January 2010 event showed greater sensitivity in the location and amount of icing due to the warm layer evolution being more directly connected to diabatic processes over the Gulf of Mexico 24-48 hours prior. Results showed discernible impact even with comparatively small SST perturbations (e.g., climatology versus control) indicating that winter precipitation is sensitive to basin SST anomalies. This work may be of use to forecasters and regional climatologists in gaining situational awareness and recognizing the role of both large-scale synoptic and regional thermodynamic drivers of phase type and intensity. Furthermore, given the observed increases in SST resulting from global climate change, this work provides physical understanding of processes that may impact ice storm evolutions in a warming climate, particularly with respect to the warm layer

Influence of the North Atlantic Subtropical High on Summer Precipitation over the Southeastern United States

<p>The Southeastern United States (SE US) is one of the fastest developing regions of the nation, where summer precipitation becomes increasingly important to sustain population and economic growth. In recent decades, the variability of SE US summer precipitation has significantly intensified, leading to more frequent and severe climate extremes. However, the processes that have caused such enhanced climate variability have been poorly understood. By analyzing atmospheric hydrological cycle, diagnosing atmospheric circulation dynamics, and performing regional climate simulations, this dissertation investigates the mechanisms responsible for SE US summer precipitation variability. </p><p>Analysis of regional moisture budget indicates that the variability of SE US summer precipitation is primarily controlled by moisture transport processes associated with the variation of the North Atlantic Subtropical High (NASH) western ridge, while local water recycling is secondary. As the ridge moves northwestward (NW) into the US continent, moisture transport pathway is away from the SE US and the upward motion is depressed. Thus, rainfall decreases over the SE US, leading to dry summers. In contrast, when the ridge moves southwestward (SW), moisture convergence tends to be enhanced over the SE US, facilitating heavier rainfall and causing wetter summers. However, as the ridge is located relatively eastward, its influence on the summer precipitation is weakened. The intensified precipitation variability in recent decades is attributed to the more frequent occurrence of NW- and SW-type ridges, according to the "NASH western ridge - SE US summer precipitation" relationship. </p><p>In addition, the "NASH western ridge - SE US summer precipitation" relationship acts as a primary mechanism to determine general circulation model (GCM) and regional climate model (RCM) skill in simulating SE US summer precipitation. Generally, the state-of-the-art GCMs that are capable of representing the abovementioned relationship perform better in simulating the variability of SE US summer precipitation. Similarly, the RCM simulated summer precipitation bias over the SE US is largely caused by the errors in the NASH western ridge circulation, with the physical parameterization playing a secondary role. </p><p>Furthermore, the relationship between the NASH western ridge and SE US summer precipitation well explains the projected future precipitation changes. According to the projection by the ensemble of phase-5 of Coupled Model Intercomparison Project (CMIP5) models, summer precipitation over the SE US will become more variable in a warming climate. The enhancement of precipitation variability is due mainly to the atmospheric circulation dynamics, resulting from the pattern shift of the NASH western ridge circulation. In a warming climate, the NASH circulation tends to intensify, which forces its western ridge to extend further westward, exerting stronger impact on the SE US summertime climate. As the ridge extends westward, the NW- and SW-type ridges occur more frequently, resulting in an increased occurrence of extreme summers over the SE US. </p><p>In summary, the studies presented in this dissertation identify the NASH western ridge as a primary regulator of SE US summer precipitation at seasonal scale. The "NASH western ridge - SE US summer precipitation" relationship established in this study serves as a first order mechanism for understanding and simulating processes that influence the statistics of extreme events over the SE in the current and future climate.</p>Dissertatio