Extending Continuum Models for Atom Probe Simulation

This work describes extensions to existing level-set algorithms developed for application within the field of Atom Probe Tomography (APT). We present a new simulation tool for the simulation of 3D tomographic volumes, using advanced level set methods. By combining narrow-band, B-Tree and particle-tracing approaches from level-set methods, we demonstrate a practical tool for simulating shape changes to APT samples under applied electrostatic fields, in three dimensions. This work builds upon our previous studies by allowing for non-axially symmetric solutions, with minimal loss in computational speed, whilst retaining numerical accuracy

Implications of single-neuron gain scaling for information transmission in networks

Summary: 

Many neural systems are equipped with mechanisms to efficiently encode sensory information. To represent natural stimuli with time-varying statistical properties, neural systems should adjust their gain to the inputs' statistical distribution. Such matching of dynamic range to input statistics has been shown to maximize the information transmitted by the output spike trains (Brenner et al., 2000, Fairhall et al., 2001). Gain scaling has not only been observed as a system response property, but also in single neurons in developing somatosensory cortex stimulated with currents of different amplitude (Mease et al., 2010). While gain scaling holds for cortical neurons at the end of the first post-natal week, at birth these neurons lack this property. The observed improvement in gain scaling coincides with the disappearance of spontaneous waves of activity in cortex (Conheim et al., 2010).

We studied how single-neuron gain scaling affects the dynamics of signal transmission in networks, using the developing cortex as a model. In a one-layer feedforward network, we showed that the absence of gain control made the network relatively insensitive to uncorrelated local input fluctuations. As a result, these neurons selectively and synchronously responded to large slowly-varying correlated input--the slow build up of synaptic noise generated in pacemaker circuits which most likely triggers waves. Neurons in gain scaling networks were more sensitive to the small-scale input fluctuations, and responded asynchronously to the slow envelope. Thus, gain scaling both increases information in individual neurons about private inputs and allows the population average to encode the slow fluctuations in the input. Paradoxically, the synchronous firing that corresponds to wave propagation is associated with low information transfer. We therefore suggest that the emergence of gain scaling may help the system to increase information transmission on multiple timescales as sensory stimuli become important later in development. 

Methods:

Networks with one and two layers consisting of hundreds of model neurons were constructed. The ability of single neurons to gain scale was controlled by changing the ratio of sodium to potassium conductances in Hodgkin-Huxley neurons (Mainen et al., 1995). The response of single layer networks was studied with ramp-like stimuli with slopes that varied over several hundreds of milliseconds. Fast fluctuations were superimposed on this slowly-varying mean. Then the response to these networks was tested with continuous stimuli. Gain scaling networks captured the slow fluctuations in the inputs, while non-scaling networks simply thresholded the input. Quantifying information transmission confirmed that gain scaling neurons transmit more information about the stimulus. With the two-layer networks we simulated a cortical network where waves could spontaneously emerge, propagate and degrade, based on the gain scaling properties of the neurons in the network

DF-Fit : A robust algorithm for detection of crystallographic information in Atom Probe Tomography data

We report on a new algorithm for detection of crystallographic information in 3D, as retained in Atom Probe Tomography (APT), with improved robustness and signal detection performance. The algorithm is underpinned by 1D distribution functions, as per existing algorithms, but eliminates an unnecessary parameter as compared to current methods. By examining traditional distribution functions in an automated fashion in real space, rather than using Fourier transform approaches, we utilise an error metric based upon the expected value for a spatially random distribution for detecting crystallography. We show cases where the metric is able to successfully obtain orientation information, and show that it can function with high levels of additive and displacive background noise. We additionally compare this metric to Fourier transform methods, showing fewer artefacts when examining simulated datasets. An extension of the approach is used to aid the automatic detection of high-quality data regions within an entire dataset, albeit with a large increase in computational cost. This extension is demonstrated on acquired Aluminium and Tungsten APT datasets, and shown to be able to discern regions of the data which have relatively improved spatial data quality. Finally, this program has been made available for use in other laboratories undertaking their own analyses

Limit-(quasi)periodic point sets as quasicrystals with p-adic internal spaces

Model sets (or cut and project sets) provide a familiar and commonly used method of constructing and studying nonperiodic point sets. Here we extend this method to situations where the internal spaces are no longer Euclidean, but instead spaces with p-adic topologies or even with mixed Euclidean/p-adic topologies. We show that a number of well known tilings precisely fit this form, including the chair tiling and the Robinson square tilings. Thus the scope of the cut and project formalism is considerably larger than is usually supposed. Applying the powerful consequences of model sets we derive the diffractive nature of these tilings.Comment: 11 pages, 2 figures; dedicated to Peter Kramer on the occasion of his 65th birthda

Weighted Dirac combs with pure point diffraction

A class of translation bounded complex measures, which have the form of weighted Dirac combs, on locally compact Abelian groups is investigated. Given such a Dirac comb, we are interested in its diffraction spectrum which emerges as the Fourier transform of the autocorrelation measure. We present a sufficient set of conditions to ensure that the diffraction measure is a pure point measure. Simultaneously, we establish a natural link to the theory of the cut and project formalism and to the theory of almost periodic measures. Our conditions are general enough to cover the known theory of model sets, but also to include examples such as the visible lattice points.Comment: 44 pages; several corrections and improvement

Practical Issues for Atom Probe Tomography Analysis of III-Nitride Semiconductor Materials.

Various practical issues affecting atom probe tomography (APT) analysis of III-nitride semiconductors have been studied as part of an investigation using a c-plane InAlN/GaN heterostructure. Specimen preparation was undertaken using a focused ion beam microscope with a mono-isotopic Ga source. This enabled the unambiguous observation of implantation damage induced by sample preparation. In the reconstructed InAlN layer Ga implantation was demonstrated for the standard "clean-up" voltage (5 kV), but this was significantly reduced by using a lower voltage (e.g., 1 kV). The characteristics of APT data from the desorption maps to the mass spectra and measured chemical compositions were examined within the GaN buffer layer underlying the InAlN layer in both pulsed laser and pulsed voltage modes. The measured Ga content increased monotonically with increasing laser pulse energy and voltage pulse fraction within the examined ranges. The best results were obtained at very low laser energy, with the Ga content close to the expected stoichiometric value for GaN and the associated desorption map showing a clear crystallographic pole structure.F.T. would like to thank David A. Nicol for his kind help. The European Research Council has provided financial support under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement No. 279361 (MACONS).This is the author accepted manuscript. The final version is available from Cambridge University Press via http://dx.doi.org/10.1017/S143192761500042

Heterogeneous integration of superconducting thin films and epitaxial semiconductor heterostructures with lithium niobate

We report on scalable heterointegration of superconducting electrodes and epitaxial semiconductor quantum dots (QDs) on strong piezoelectric and optically nonlinear lithium niobate. The implemented processes combine the sputter-deposited thin film superconductor niobium nitride and III–V compound semiconductor membranes onto the host substrate. The superconducting thin film is employed as a zero-resistivity electrode material for a surface acoustic wave resonator with internal quality factors approx Q≈17,000 representing a three-fold enhancement compared to identical devices with normal conducting electrodes. Superconducting operation of ≈400MHz resonators is achieved to temperatures T>7K and electrical radio frequency powers Prf>+9dBm. Heterogeneously integrated single QDs couple to the resonant phononic field of the surface acoustic wave resonator operated in the superconducting regime. Position and frequency selective coupling mediated by deformation potential coupling is validated using time-integrated and time-resolved optical spectroscopy. Furthermore, acoustoelectric charge state control is achieved in a modified device geometry harnessing large piezoelectric fields inside the resonator. The hybrid QD—surface acoustic wave resonator can be scaled to higher operation frequencies and smaller mode volumes for quantum phase modulation and transduction between photons and phonons via the QD. Finally, the employed materials allow for the realization of other types of optoelectronic devices, including superconducting single photon detectors and integrated photonic and phononic circuits