Built-in Self-Test and Fault Localization for Inter-Layer Vias in Monolithic 3D ICs

Monolithic 3D (M3D) integration provides massive vertical integration through the use of nanoscale inter-layer vias (ILVs). However, high integration density and aggressive scaling of the inter-layer dielectric make ILVs especially prone to defects. We present a low-cost built-in self-test (BIST) method that requires only two test patterns to detect opens, stuck-at faults, and bridging faults (shorts) in ILVs. We also propose an extended BIST architecture for fault detection, called Dual-BIST, to guarantee zero ILV fault masking due to single BIST faults and negligible ILV fault masking due to multiple BIST faults. We analyze the impact of coupling between adjacent ILVs arranged in a 1D array in block-level partitioned designs. Based on this analysis, we present a novel test architecture called Shared-BIST with the added functionality of localizing single and multiple faults, including coupling-induced faults. We introduce a systematic clustering-based method for designing and integrating a delay bank with the Shared-BIST architecture for testing small-delay defects in ILVs with minimal yield loss. Simulation results for four two-tier M3D benchmark designs highlight the effectiveness of the proposed BIST framework.

Future Property Risk Estimation For Wildfire In Louisiana, USA

Background: Wildfire is an important but understudied natural hazard. As with other natural hazards, wildfire research is all too often conducted at too broad a spatial scale to identify local or regional patterns. This study addresses these gaps by examining the current and future wildfire property risk at the census-block level in Louisiana, a U.S. state with relatively dense population and substantial vulnerability to loss from this hazard, despite its wet climate. Here wildfire risk is defined as the product of exposure and vulnerability to the hazard, where exposure is a function of the historical and anticipated future wildfire frequency and extent, and the latter is a function of population, structure and content property value, damage probability, and percent of property damaged. Results: Historical (1992−2015) average annual statewide property loss due to wildfire was $5,556,389 (2010$), with the greatest risk to wildfire in southwestern inland, east-central, extreme northwestern, and coastal southwestern Louisiana. Based on existing climate and environmental model output, this research projects that wildfire will increase by 25 percent by 2050 in Louisiana from current values. When combined with projections of population and property value, it is determined that the geographic distribution of risk by 2050 will remain similar to that today – with highest risk in southwestern inland Louisiana and east-central Louisiana. However, the magnitude of risk will increase across the state, especially in those areas. Projected annual loss will be $11,167,496 by 2050 (2010$) due to population growth, intensification of development at the wildland-urban interface, and climate change. The wildfire-induced property damage is notable because it is projected to increase by 101 percent. These values do not include crop, forestry, or indirect losses (e.g., cost of evacuation and missed time at work), which are likely to be substantial. Conclusions: The results suggest that increased efforts are needed to contain wildfires, to reduce the future risk. Otherwise, wildfire managers, environmental planners, actuaries, community leaders, and individual property owners in Louisiana will need to anticipate and budget for additional efforts to mitigate the economic (and presumably other) impacts associated with a substantial and increasing hazard that often goes underestimated.

SERVICE CENTRES IN RURAL DEVELOPMENT : A CASE STUDY OF JAUNPUR DISTRICT, UTTAR PRADESH

This paper attempts to deal with the identifying the service centers and calculation of the spatial arrangement with complementary area of service centres in Jaunpur district Jaunpur district of Uttar Pradesh. The study area is situated in Eastern Uttar Pradesh of the Middle Ganga Plain. The study is exclusively based on secondary data collected at block level from different offices. The centrality score has been calculated on the basis of three type of indices like functional centrality index, working population index and tertiary population index. There are 31 function or services selected judicially from five sectors (administrative, agricultural and financial, educational, health and transport and communication) to measure the centrality of service centre. The thissen polygon and berry breaking point method has been used for measure the complementary area. Total 88 service centres have been identified as first, second, third, fourth and fifth order service centre. The number of I, II, III, IV, and V order centres accounts for 43, 24, 16, 4, and 1 respectively.

Relevance of developing public health service cadres alongside a more prevention oriented Indian health system: an overview

The relevance of public health has been emphasized in the wake of the global epidemic COVID-19. There are several success stories that we often tend to forget, such as the fight against various infectious illnesses like smallpox, poliomyelitis, and current human-immunodeficiency virus (HIV) prevention, to name a few, public health has played a significant impact. Diarrheal diseases, for example, which contributes significantly to India's under-five mortality rate and is one of the leading causes of malnutrition, can be effectively handled by improving access to safe water and sanitation. Because public health encompasses more than just health, we require a workforce with managerial and leadership skills as well as training in public health as a specialty. This paper explores some of the successes and lessons learned from systematic investments in public health in the Indian state of Tamil Nadu, namely The Tamil Nadu model and other countries, as well as the system's flaws. In India, a feasible framework for establishing dedicated public health cadres has also been explored. Evidence was acquired from PubMed, Google Scholar, newspaper stories, and publicly released government orders and papers. The recruitment of cadres may resemble that of the Indian economic/statistical services (IES/ ISS) by the UPSC. Another area to emphasize for health professionals interested in public health is training. Starting from frontline workers, block level workers to district and state we need dedicated public health workforce. Moreover, the need of the hour is to establish such a system which will work alongside pre-existing clinical fields.

Residential Vulnerability of Barcelona: Methodology Integrating Multi-Criteria Evaluation Systems and Geographic Information Systems

This article presents the methodology and results of a pioneering investigation in the determination and mapping of socio-residential vulnerability in the city of Barcelona according to a multi-criteria synthetic analysis. The methodology followed is based on a system of indicators elaborated from the exploitation of habitual statistical Open Data complemented with specific unprecedented data elaborated and supplied by the Barcelona City Council. The analysis is based on secondary data and it is structured in georeferenced axes, components, and indicators, which allow determination of the sociodemographic, socioeconomic, and urban and residential space characteristics at neighborhood, population census unit, and urban block level. The objective of the research was to detect, determine, and establish a measure of differentiation relative to the degree of residential vulnerability of some neighborhoods with respect to others, in order to seek prioritization measures for action in the most vulnerable areas. The results of the research provide a series of maps that allow us to define the areas where the highest levels of vulnerability indicators coincide according to a synthetic multi-criteria analysis.

Improving assessment of urban racial segregation by partitioning a region into racial enclaves

Frequently, a single-value metric is needed to rank urban regions with respect to the level of multiracial segregation or to compare a segregation level of a single urban region at two different times. Assessment of segregation depends not only on a metric used but also on a choice of region’s partitioning. The standard practice is to partition the region into single-scale subregions. In the United States, census tracts are the subregions of choice. Census aggregation units including tracts are delineated without direct regard to racial homogeneity and are in fact heterogeneous. Consequently, using tracts as subdivisions leads to the underestimation of the segregation level of the entire region. Here we propose to partition a region into racial enclaves - units having boundaries that align with transitions between different racial compositions. By reflecting true demographic structure, such units minimize their internal racial inhomogeneity resulting in improved assessment of segregation. Enclaves are defined as aggregates of adjacent census blocks (smallest and the most racially homogeneous census units) of similar composition. In a typical US urban region effective population size of enclaves is an order of magnitude larger than the size of a census tract and yet the segregation calculated based on enclaves is larger than segregation based on census tracts. The proposed methodology is described and applied to a set of 61 largest cities in the U.S. in their metropolitan statistical areas (MSAs) as well as their urban areas (UAs) boundaries using 1990 and 2010 block-level data. The method is compared to the standard methodology using correlations between cities’ segregation rankings.

Geography of racial coresidence

Whereas most work on residential race relations in US cities is based on the concept of segregation, our approach studies this issue from a who-lives-with-whom perspective. To this end, we study coresidence profiles – percentages of a given racial subpopulation living in different population zones. Population zones are data-driven divisions of a city based on characteristic racial compositions. We used 1990 and 2010 decennial census block-level data for 61 largest US metropolitan areas to calculate coresidence profiles for four major racial subpopulations in each city at both years. Profiles for each race/year combination were clustered into three archetypes. Cities, where given race profiles belong to the same archetype, have similar coresidence patterns with respect to this race. We present the geographic distributions of co-habitation profiles and show how they changed during the 1990-2010 period. Our results revealed that coresidence profiles depend not only on racial preferences but also on the availability of racial groups; cities in the different geographical regions have different coresidence profiles because they have different shares of White, Black, Hispanic, and Asian subpopulations. Temporal changes in coresidence profiles are linked to the increased share of Hispanic and Asian populations.