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High resolution CMOS image sensor demand is driven by consumer applications like mobile phone or digital still camera. To maintain the image quality, we have to save the signal to noise ratio (SNR) despite the number of photons reduction gathered by the pixel. In this paper, we present how back-side illumination technology could help to recover signal and how to optimize the process and device in order to reduce the noise. A quantum efficiency larger than 70% is targeted to justify efforts done on BSI process development. We also have to pay attention to pixel to pixel crosstalk coming from free carrier diffusion. To overcome this parasitic phenomenon, deep trench isolation (DTI) should be considered in order to significantly reduce crosstalk between two neighboring pixels. The second main noise contributor related to BSI process is the dark current signal. Dark current less than 1 aA per pixel at room temperature is mandatory to save the image quality at low light level. Therefore, all the Si-SiO2 interfaces at the pixel side walls have to be processed without any process damage to reduce as much as possible the surface defects. On top of that, side wall implantation and side wall dielectric fabrication have been optimized to reduce this noise source. The pixel layout has to be fully optimized also to collect and store charges properly. With respect to the 3D pixel shape, a new fully depleted pinned photodiode with vertical charge storage and no lag has been developed. (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Ground beetle (Carabidae) assemblages are speciose and frequently employed as indicators of ecosystem function. It is thus important to understand the factors that affect their spatial distributions so as to better interpret how ecosystem variation influences the structure of their assemblages. We evaluated how anthropogenic disturbances, habitat heterogeneity, and spatial autocorrelation (unmeasured spatially structured habitat variables and/or dispersal limitation) influence the distribution of carabids on a mature boreal forest landscape. We worked with a large sample of carabids from 194 sites from a near-regular grid covering 70 km2. Data about forest floor cover, vegetation structure, soil drainage, and topography were associated with each site. We modelled the structure of carabid assemblages using these variables together with Moran's eigenvector maps (MEM) as spatial constraints. Overall, our model explained about half of the variation in ground beetle assemblages. Forest floor cover, soil drainage, and vegetation structure were the principal factors useful for explaining carabid assemblages. The spatial patterns described by the MEMs for the ground beetle assemblages seem to result from spatial autocorrelation in soil drainage, floor cover, topography, and vegetation structure; however, this spatial autocorrelation independently and uniquely explained only 1.4% of the variation in assemblages. This was much less important than the explanatory power of the environmental variables considered; e.g. ground cover descriptors and vegetation structure by themselves accounted, respectively, for 12.1 and 4.5% of the variation in the carabid data. The spatial distribution of ground beetles in undisturbed forest was only mildly affected by surrounding anthropogenic disturbances, but clearly small patches were invaded by species characteristic of open habitat. We conclude that to help conserve ground beetle diversity at the landscape scale in boreal forests, mature forest patches should be large and connected to other patches and the surrounding forest. [ABSTRACT FROM AUTHOR]

The optimization of the small and large signal performances of a radio frequency (RF)-LDMOS is presented via the achievement of a novel LDMOS architecture. Specific process steps are introduced into a 0.25-µm BiCMOS technology and precisely described to realize a fully salicided gate RF-LDMOS architecture. Significant improvement is obtained on the small-signal—fT and Fmax—and power performances while maintaining good dc characteristics. [ABSTRACT FROM AUTHOR]

Current analyses of metacommunity data largely focus on global attributes across the entire metacommunity, such as mean alpha, beta, and gamma diversity, as well as the partitioning of compositional variation into single estimates of contributions of space and environmental effects and, more recently, possible contributions of species interactions. However, this view neglects the fact that different species and sites in the landscape can vary widely in how they contribute to these metacommunity‐wide attributes. We argue for a new conceptual framework with matched analytics with the goals of studying the complex and interactive relations between process and pattern in metacommunities that is focused on the variation among species and among sites which we call the 'internal structure' of the metacommunity. To demonstrate how the internal structure could be studied, we create synthetic data using a process‐based colonization–extinction metacommunity model. We then use joint species distribution models to estimate how the contributions of space, environment, and biotic interactions driving metacommunity assembly differ among species and sites. We find that this approach to the internal structure of metacommunities provides useful information about the distinct ways that different species and different sites contribute to metacommunity structure. Although it has limitations, our work points at a more general approach to understand how other possible complexities might affect internal structure and might thus be incorporated into a more cohesive metacommunity theory. [ABSTRACT FROM AUTHOR]

Aim: Food webs are essential for understanding how ecosystems function, but empirical data on the interactions that make up these ecological networks are lacking for most taxa in most ecosystems. Trait‐based models can fill these data gaps, but their ability to do so has not been widely tested. We test how well these models can extrapolate to new ecological communities both in terms of pairwise predator–prey interactions and higher level food web attributes (i.e. species position, food web‐level properties). Location: Canada, Europe, Tanzania. Time Period: Current. Major Taxa Studied: Terrestrial vertebrates. Methods: We train trait‐based models of pairwise trophic interactions on four independent vertebrate food webs (Canadian tundra, Serengeti, alpine south‐eastern Pyrenees and Europe) and evaluate how well these models predict pairwise interactions and network properties of each food web. Results: We find that, overall, trait‐based models predict most interactions and their absence correctly. Performance was best for training and testing on the same food web (AUC > 0.90) and declined with environmental and phylogenetic distances with the strongest loss of performance for the tundra‐Serengeti ecosystems (AUC > 0.75). Network metrics were less well‐predicted than single interactions by our models with predicted food webs being more connected, less modular, and with higher mean trophic levels than observed. Main Conclusions: Theory predicts that the variability observed in food webs can be explained by differences in trait distributions and trait‐matching relationships. Our finding that trait‐based models can predict many trophic interactions, even in contrasting environments, adds to the growing body of evidence that there are general constraints on interactions and that trait‐based methods can serve as a useful first approximation of food webs in unknown areas. However, food webs are more than the sum of their parts, and predicting network attributes will likely require models that simultaneously predict individual interactions and community constraints. [ABSTRACT FROM AUTHOR]

Recently developing hierarchical community models (HCMs) accounting for incomplete sampling are promising approaches to understand community organization. However, pros and cons of incorporating incomplete sampling in the analysis and related design issues remain unknown. In this study, we compared HCM and canonical redundancy analysis (RDA) carried out with 10 different dissimilarity coefficients to evaluate how each approach restores true community abundance data sampled with imperfect detection. We conducted simulation experiments with varying numbers of sampling sites, visits, mean detectability and mean abundance. Performance of HCM was measured by estimates of "expected" (mean) abundance (λ^ij) and realized abundance (N^ij: direct estimate of site‐ and species‐specific abundance). We also compared HCM and different types of RDA (normal, partial, and weighted), all performed with the same ten different dissimilarity coefficients, with unequal number of visits to sampling sites. In addition, we applied the models to a virtual survey carried out on the Barro Colorado Island tree plot data for which we know true community abundance. Simulation experiments showed that N^ij yielded by HCM best restored the underlying abundance of constituent species among 12 abundance estimates by HCM and RDA regardless if the sampling was equal or unequal. Mean abundance predominantly affected the performance of HCM and RDA while λ^ij yielded by HCM had comparable performance to percentage difference and Gower dissimilarity coefficients of RDA. Relative performance of RDA types depended on the combination of dissimilarity coefficients and the distribution of sampling effort. Best performance of N^ij followed by λ^ij, percentage difference and Gower dissimilarity were also observed for the analysis of tree plot data, and graphical plots (triplots) based on λ^ij rather than N^ij clearly separated the effects of two environmental covariates on the abundance of constituent species. Under our conditions of model evaluation and the method, we concluded that, in terms of assessing the environmental dependence of abundance, HCMs and RDA can have comparable performance if we can choose appropriate dissimilarity coefficients for RDA. However, since HCMs provide straightforward biological interpretations of parameter estimates and flexibility of the analysis, HCMs would be useful in many situations as well as conventional canonical ordinations. [ABSTRACT FROM AUTHOR]

A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade‐offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross‐validation procedure involving separate data to establish which of these models performs best for the goal of the study. [ABSTRACT FROM AUTHOR]