CHIP: Clustering hotspots in layout using integer programming

Clustering algorithms have been explored in recent years to solve hotspot clustering problem in Integrated Circuit design. With various applications in Design for Manufacturability flow such as hotspot library generation, systematic yield optimization and design space exploration, generating good quality clusters along with their representative clips is of utmost importance. With several generic clustering algorithms at our disposal, hotspots can be clustered based on the distance metric defined while satisfying some tolerance conditions. However, the clusters generated from generic clustering algorithms need not achieve optimal results. In this paper, we introduce two optimal integer linear programming formulations based on triangle inequality to solve the problem of minimizing cluster count while satisfying given constraints. Apart from minimizing cluster count, we generate representative clips that best represent the clusters formed. We achieve better cluster count for both formulations in most test cases as compared to the results published in literature on the ICCAD 2016 contest benchmarks as well as the reference results reported in the ICCAD 2016 contest websit

Pattern classification for layout hotspots

The final objective of an integrated circuit design is to produce a layout, that is, a geometrical representation of the circuit where the geometrical shapes correspond to patters that will be formed by layers of metal, oxide, and semiconductors. These patterns are essentially descriptions that will be used to print the circuit through chemical, thermal and photographic processes. To ensure the layout can be used to print the circuit with no defects, it is necessary to run design rules check. This verification searches for patterns that violate design rules, which makes it impossible to guarantee defect-free printing. However, some layout patterns may present printability problems even when design rules are respected. To solve this problem, physical verification flows are applied to the layout with the objective of detecting and treating such patterns. The sheer number of these layout printability hotspots and the fact that they are sometimes similar to each other suggests that the physical verification flow can be sped up by clustering together similar patterns. In this work, we address the problem of complex shape partitioning, incorporating an algorithm with complexity O(n5=2) into the layout hotspot clustering flow, which allows for clustering of hotspots in benchmarks with complex polygons. Furthermore, a study of the viability of a machine learning flow for incremental clustering is conducted, covering the choice of features and analysis of candidate models.O objetivo final do fluxo de projeto de um circuito é produzir um leiaute, uma representação geométrica do circuito, onde as formas geométricas correspondem aos padrões que serão formados por camadas de metal, óxido e semicondutores. Esses padrões são essencialmente descrições que serão usadas para imprimir o circuito através de processos químicos, térmicos e fotográficos. Para garantir que o leiaute possa ser usado para impressão de um circuito integrado sem defeitos, é necessário executar verificações de regras de projeto. Essa verificação encontra padrões que violam regras que inviabilizariam a garantia de impressão sem defeitos. Porém, alguns padrões do leiaute podem apresentar problemas na impressão mesmo quando a checagem das regras de projeto não encontra erros. Para solucionar esse problema, fluxos de verificação física são aplicados no leiaute com o objetivo de detectar e tratar tais padrões. A grande quantidade de regiões com problemas de impressão e a similaridade entre elas sugere que o fluxo de verificação física pode ser acelerado ao se agrupar padrões similares. Neste trabalho, o problema de particionamento de polígonos complexos é abordado, e um algoritmo de particionamento de complexidade O(n5=2) é incorporado ao fluxo de classificação e agrupamento de regiões de interesse, permitindo que casos de teste com polígonos complexos tenham suas regiões de interesse agrupadas. Além disso, um estudo sobre a viabilidade de um fluxo de aprendizado de máquina é conduzido, cobrindo a escolha de atributos e a análise de diferentes modelos candidatos

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

Lithography aware physical design and layout optimization for manufacturability

textAs technology continues to scale down, semiconductor manufacturing with 193nm lithography is greatly challenging because the required half pitch size is beyond the resolution limit. In order to bridge the gap between design requirements and manufacturing limitations, various resolution enhancement techniques have been proposed to avoid potentially problematic patterns and to improve product yield. In addition, co-optimization between design performance and manufacturability can further provide flexible and significant yield improvement, and it has become necessary for advanced technology nodes. This dissertation presents the methodologies to consider the lithography impact in different design stages to improve layout manufacturability. Double Patterning Lithography (DPL) has been a promising solution for sub-22nm node volume production. Among DPL techniques, self-aligned double patterning (SADP) provides good overlay controllability when two masks are not aligned perfectly. However, SADP process places several limitations on design flexibility and still exists many challenges in physical design stages. Starting from the early design stage, we analyze the standard cell designs and construct a set of SADP-aware cell placement candidates, and show that placement legalization based on this SADP awareness information can effectively resolve DPL conflicts. In the detailed routing stage, we propose a new routing cost formulation based on SADP-compliant routing guidelines, and achieve routing and layout decomposition simultaneously. In the case that limited routing perturbation is allowed, we propose a post-routing flow based on lithography simulation and lithography-aware design rules. Both routing methods, one in detailed routing stage and one in post routing stage, reduce DPL conflicts/violations significantly with negligible wire length impact. In the layout decomposition stage, layout modification is restricted and thus the manufacturability is even harder to guaranteed. By taking the advantage of complementary lithography, we present a new layout decomposition approach with e-beam cutting, which optimizes SADP overlay error and e-beam lithography throughput simultaneously. After the mask layout is defined, optical proximity correction (OPC) is one of the resolution enhancement techniques that is commonly required to compensate the image distortion from the lithography process. We propose an inverse lithography technique to solve the OPC problem considering design target and process window co-optimization. Our mask optimization is pixel based and thus can enable better contour fidelity. In the final physical verification stage, a complex and time-consuming lithography simulation needs to be performed to identify faulty patterns. We provide a classification method based on support vector machine and principle component analysis that detects lithographic hotspots efficiently and accurately.Electrical and Computer Engineerin

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Geo-information based model for assessing and monitoring forest fire risk in the state of Missouri, United States of America

Title from PDF of title page viewed July 19, 2021Dissertation advisors: Jimmy Adegoke and Linwood TauheedVitaIncludes bibliographical references (pages 62-73)Thesis (Ph.D.)--Department of Geosciences and Social Science Consortium. University of Missouri--Kansas City, 2020Despite the fact that a lot of resources has been invested in fire protection and suppression, the number of fires recurring in Missouri in recent decades has continued to markedly increase. Much of forest research has focused on the biological and physical aspects of fire with comparatively less attention given to the importance of socio-economic variables and risk assessment. There is therefore the need to develop a framework for the assessment and monitoring of forest fire risk which is presently lacking in the state of Missouri. This is where this study derives its relevance. Missouri is currently ranked among the top seven states ravaged by wildfires in the United States. The specific objectives are to apply a geoinformation based model for the assessment of wildfire risk in Missouri; assess social vulnerability to wildfire, analyze the relationship between climate variability and wildfires; and examine wildfire policy in the United States, and the implications for wildfire risk reduction in Missouri. Forest risk and vulnerability assessment of Missouri was undertaken using some measurable environmental parameters influencing forest fire risk and vulnerability. Using the four ecological zones in Missouri and geospatial model as the basis of analysis, three forest risk zones were identified. These are high forest fire risk zones, moderate forest fire risk zone and low forest fire risk zone. Also, social vulnerability to wildfire risk in Missouri was assessed with the American Community Survey data on social and demographic variables for the state of Missouri and social vulnerability index (S0VI). The study divided Missouri into five geopolitical zones from which ten counties were randomly selected for this study. The selected counties formed the basis on which fourteen social and demographic indicators were identified and assessed using Bogardi, Birkmann and Cadona conceptual framework. The result of the analysis shows that S0VI estimated for the five geopolitical zones of Missouri is moderate with a rating scale of 1.42 – 1.71. Education, income and marital status have a rating scale of 2.0 - 3.0 attributed for the high value of Social Vulnerability to wildfire. Race / ethnicity, language spoken, employment and percentage of house units that are mobile homes had a low S0VI value of 1.0 thereby contributing positively to resilience to wildfire risk. The relationship between climate variability and wildfire occurrence in Missouri was analyzed by examining the correlation between wildfire seasonality frequency, acres burned, and temperature from 1995 to 2018 using Pearson correlation method. The results reveal no significant correlation between climate and wildfire occurrence in Missouri. However, the study observes that other factors such as arson arising from human activities could have contributed to wildfire occurrence in Missouri. Finally, the study examines the failure of wildfire mitigation policy framework in the state, and how this has impacted wildfire mitigation efforts in the state of Missouri. The study concludes that though government involvement in wildfire risk reduction is quite impressive, there is no policy framework at the local and state level towards combating wildfire hazards. This becomes necessary because wildfire in Missouri is human induced caused majorly by arson. The current social and demographic characteristics of forest landowners, land use change, wildland-urban-interface, ecological and climate change are critical factors that must be put into consideration in formulating effective and sustainable wildfire policy reduction initiatives in Missouri.Introduction -- Assessment of forest fire vulnerability zones in Missouri, United States of America -- Assessment of social vulnerability to wildfire in Missouri, United States of America -- Climate and wildfire in Missouri, United States of America -- Wildfire policy challenge in the United States: Implications of wildfire risk reduction in Missouri -- Conclusio

Forest cover and its change in Unguja Island, Zanzibar

Tropical forests are sources of many ecosystem services, but these forests are vanishing rapidly. The situation is severe in Sub-Saharan Africa and especially in Tanzania. The causes of change are multidimensional and strongly interdependent, and only understanding them comprehensively helps to change the ongoing unsustainable trends of forest decline. Ongoing forest changes, their spatiality and connection to humans and environment can be studied with the methods of Land Change Science. The knowledge produced with these methods helps to make arguments about the actors, actions and causes that are behind the forest decline. In this study of Unguja Island in Zanzibar the focus is in the current forest cover and its changes between 1996 and 2009. The cover and changes are measured with often used remote sensing methods of automated land cover classification and post-classification comparison from medium resolution satellite images. Kernel Density Estimation is used to determine the clusters of change, sub-area –analysis provides information about the differences between regions, while distance and regression analyses connect changes to environmental factors. These analyses do not only explain the happened changes, but also allow building quantitative and spatial future scenarios. Similar study has not been made for Unguja and therefore it provides new information, which is beneficial for the whole society. The results show that 572 km2 of Unguja is still forested, but 0,82–1,19% of these forests are disappearing annually. Besides deforestation also vertical degradation and spatial changes are significant problems. Deforestation is most severe in the communal indigenous forests, but also agroforests are decreasing. Spatially deforestation concentrates to the areas close to the coastline, population and Zanzibar Town. Biophysical factors on the other hand do not seem to influence the ongoing deforestation process. If the current trend continues there should be approximately 485 km2 of forests remaining in 2025. Solutions to these deforestation problems should be looked from sustainable land use management, surveying and protection of the forests in risk areas and spatially targeted self-sustainable tree planting schemes.Siirretty Doriast

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Developing A Geospatial Protocol For Coral Epizootiology

This dissertation explores how geographic information systems (GIS) and spatial statistics, specifically the techniques used to map, detect, and spatially analyze disease epidemics, could be used to advance our understanding of coral reef health. Given that different types of spatial analysis, as well as different parameter settings within each analysis, can produce noticeably different results, poor selection or improper use of a given technique would likely lead to inaccurate representations of the spatial distribution and false interpretations of the disease. For this reason, I performed a comprehensive review of the following types of exploratory spatial data analysis (ESDA): mapping and visualization methods; centrographic and distance-based point pattern analyses; spatial kernel density estimates (KDE) using single and dual versions of adaptive and fixed-distance KDEs in which the fixed-distance KDEs were performed using bandwidths calculated using 12 different estimation methods; SaTScan’s spatial scan statistic using both the Bernoulli and Poisson probability models; and last, local and global versions of the Moran’s I and Getis-ord G spatial autocorrelation statistics. Each technique was applied to an artificial dataset with known cluster locations in order to determine which methods provided the most accurate results. These results were then used to develop different geospatial analytical protocols based on the types of coral data available, noting that the most meaningful results would be produced using local spatial statistics to analyze data of diseased colonies and colonies from the underlying coral population at risk. Last, I applied the techniques from one of the protocols to data from a 2004 White-Band Disease (WBD) outbreak on a population of Acropora palmata corals in the US Virgin Islands. The results of this work represent the first application of geospatial analytical techniques in visualizing the spatial nature of a coral disease and provides important information about the epizootiology of this particular outbreak. Specifically, the results indicated that WBD prevalence was low with numerous significant disease clusters occurring throughout the study area, suggesting WBD may be caused by a ubiquitous stressor. The material presented in this dissertation will provide researchers with the necessary tools and information needed to perform the most accurate geospatial analysis possible based on the coral data available