Measuring dopant concentrations in compensated p-type crystalline silicon via iron-acceptor pairing

We present a method for measuring the concentrations of ionized acceptors and donors in compensated p-type silicon at room temperature.Carrier lifetimemeasurements on silicon wafers that contain minute traces of iron allow the iron-acceptor pair formation rate to be determined, which in turn allows the acceptor concentration to be calculated. Coupled with an independent measurement of the resistivity and a mobility model that accounts for majority and minority impurity scatterings of charge carriers, it is then possible to also estimate the total concentration of ionized donors. The method is valid for combinations of different acceptor and donor species.D.M. is supported by an Australian Research Council fellowship. L.J.G. would like to acknowledge SenterNovem for support

Recombination activity of interstitial iron and other transition metal point defects in p- and n-type crystalline silicon

Interstitial iron in crystalline silicon has a much larger capture cross section for electrons than holes. According to the Shockley–Read–Hall model, the low-injection carrier lifetime in p-type silicon should therefore be much lower that in n-type silicon, while in high injection they should be equal. In this work we confirm this modeling using purposely iron-contaminated samples. A survey of other transition metal impurities in silicon reveals that those which tend to occupy interstitial sites at room temperature also have significantly larger capture cross sections for electrons. Since these are also the most probable metal point defects to occur during high temperature processing, using n-type wafers for devices such as solar cells may offer greater immunity to the effects of metal contaminants.This work has been supported by the Australian Research Council and The Netherlands Agency for Energy and the Environment

Dynamics of light-induced FeB pair dissociation in crystalline silicon

The dynamics of light-induced dissociation of iron–boron (FeB) pairs in p-type crystalline silicon is investigated. The dissociation is observed to be a single-exponential process which is balanced with thermal repairing. The dissociation rate is proportional to the square of the carrier generation rate and the inverse square of the FeB concentration. This suggests that the dissociation process involves two recombination or electron capture events. A proportionality constant of 5×10⁻¹⁵s describes the dissociation rate well in the absence of other significant recombination channels. The dissociation rate decreases in the presence of other recombination channels. These results can be used for reliable detection of iron in silicon devices and materials, and for further elucidation of the electronically driven FeB dissociation reaction.This work was supported by NOVEM (The Netherlands Agency for Energy and the Environment) and the Australian Research Council

Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models

The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain's functional connectivity structure across a group of young individuals and a group of old individuals

Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models.

The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain's functional connectivity structure across a group of young individuals and a group of old individuals

Sum rule for transport in a Luttinger liquid with long range interaction in the presence of an impurity

We show that the non-linear dc transport in a Luttinger liquid with interaction of finite range in the presence of an impurity is governed by a sum rule which causes the charging energy to vanish.Comment: 5 pages, RevTeX, 1 figure, to be published in Europhysics Letter

Are visual working memory and episodic memory distinct processes? Insight from stroke patients by lesion-symptom mapping

Working memory and episodic memory are two different processes, although the nature of their interrelationship is debated. As these processes are predominantly studied in isolation, it is unclear whether they crucially rely on different neural substrates. To obtain more insight in this, 81 adults with sub-acute ischemic stroke and 29 elderly controls were assessed on a visual working memory task, followed by a surprise subsequent memory test for the same stimuli. Multivariate, atlas- and track-based lesion-symptom mapping (LSM) analyses were performed to identify anatomical correlates of visual memory. Behavioral results gave moderate evidence for independence between discriminability in working memory and subsequent memory, and strong evidence for a correlation in response bias on the two tasks in stroke patients. LSM analyses suggested there might be independent regions associated with working memory and episodic memory. Lesions in the right arcuate fasciculus were more strongly associated with discriminability in working memory than in subsequent memory, while lesions in the frontal operculum in the right hemisphere were more strongly associated with criterion setting in subsequent memory. These findings support the view that some processes involved in working memory and episodic memory rely on separate mechanisms, while acknowledging that there might also be shared processes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-021-02281-0

Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation

Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on "distance correlation"; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data.The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1). LG is funded by a Rubicon grant from the Netherlands Organization for Scientific Research. RH is funded by UK Medical Research Council Programme MC-A060-5PR10

Cooper pair cotunneling in single charge transistors with dissipative electromagnetic environment

We observed current-voltage characteristics of superconducting single charge transistors with on-chip resistors of R about R_Q = h/4e^2 = 6.45 kOhm, which are explained in terms of Cooper-pair cotunneling. Both the effective strength of Josephson coupling and the cotunneling current are modulated by the gate-induced charge on the transistor island. For increasing values of the resistance R we found the Cooper pair current at small transport voltages to be dramatically suppressed.Comment: 4 pages and 2 figure