Trends and challenges in VLSI technology scaling towards 100 nm

Summary form only given. Moore's Law drives VLSI technology to continuous increases in transistor densities and higher clock frequencies. This tutorial will review the trends in VLSI technology scaling in the last few years and discuss the challenges facing process and circuit engineers in the 100nm generation and beyond. The first focus area is the process technology, including transistor scaling trends and research activities for the 100nm technology node and beyond. The transistor leakage and interconnect RC delays will continue to increase. The tutorial will review new circuit design techniques for emerging process technologies, including dual Vt transistors and silicon-on-insulator. It will also cover circuit and layout techniques to reduce clock distribution skew and jitter, model and reduce transistor leakage and improve the electrical performance of flip-chip packages. Finally, the tutorial will review the test challenges for the 100nm technology node due to increased clock frequency and power consumption (both active and passive) and present several potential solution

Bayesian and Adaptive Optimal Policy under Model Uncertainty

We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but seldom with forward-looking variables which are a key component of modern policy-relevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However, computing the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide some simple examples to illustrate the role of learning and experimentation in an MJLQ framework.

Superdeformed bands in neutron-rich Sulfur isotopes suggested by cranked Skyrme-Hartree-Fock calculations

On the basis of the cranked Skyrme-Hartree-Fock calculations in the three-dimensional coordinate-mesh representation, we suggest that, in addition to the well-known candidate 32S, the neutron-rich nucleus 36S and the drip-line nuclei,48S and 50S, are also good candidates for finding superdeformed rotational bands in Sulfur isotopes. Calculated density distributions for the superdeformed states in 48S and 50S exhibit superdeformed neutron skinsComment: 18 pages including 10 ps figure

Soft metrics and their Performance Analysis for Optimal Data Detection in the Presence of Strong Oscillator Phase Noise

In this paper, we address the classical problem of maximum-likelihood (ML) detection of data in the presence of random phase noise. We consider a system, where the random phase noise affecting the received signal is first compensated by a tracker/estimator. Then the phase error and its statistics are used for deriving the ML detector. Specifically, we derive an ML detector based on a Gaussian assumption for the phase error probability density function (PDF). Further without making any assumptions on the phase error PDF, we show that the actual ML detector can be reformulated as a weighted sum of central moments of the phase error PDF. We present a simple approximation of this new ML rule assuming that the phase error distribution is unknown. The ML detectors derived are also the aposteriori probabilities of the transmitted symbols, and are referred to as soft metrics. Then, using the detector developed based on Gaussian phase error assumption, we derive the symbol error probability (SEP) performance and error floor analytically for arbitrary constellations. Finally we compare SEP performance of the various detectors/metrics in this work and those from literature for different signal constellations, phase noise scenarios and SNR values

Nonlinear thermoelectric response due to energy-dependent transport properties of a quantum dot

Quantum dots are useful model systems for studying quantum thermoelectric behavior because of their highly energy-dependent electron transport properties, which are tunable by electrostatic gating. As a result of this strong energy dependence, the thermoelectric response of quantum dots is expected to be nonlinear with respect to an applied thermal bias. However, until now this effect has been challenging to observe because, first, it is experimentally difficult to apply a sufficiently large thermal bias at the nanoscale and, second, it is difficult to distinguish thermal bias effects from purely temperature-dependent effects due to overall heating of a device. Here we take advantage of a novel thermal biasing technique and demonstrate a nonlinear thermoelectric response in a quantum dot which is defined in a heterostructured semiconductor nanowire. We also show that a theoretical model based on the Master equations fully explains the observed nonlinear thermoelectric response given the energy-dependent transport properties of the quantum dot.Comment: Cite as: A. Svilans, et al., Physica E (2015), http://dx.doi.org/10.1016/j.physe.2015.10.00

Radiation Front Sweeping the Ambient Medium of Gamma-Ray Bursts

Gamma-ray bursts (GRBs) are emitted by relativistic ejecta from powerful cosmic explosions. Their light curves suggest that the gamma-ray emission occurs at early stages of the ejecta expansion, well before it decelerates in the ambient medium. If so, the launched gamma-ray front must overtake the ejecta and sweep the ambient medium outward. As a result a gap is opened between the ejecta and the medium that surfs the radiation front ahead. Effectively, the ejecta moves in a cavity until it reaches a radius R_{gap}=10^{16}E_{54}^{1/2} cm where E is the isotropic energy of the GRB. At R=R_{gap} the gap is closed, a blast wave forms and collects the medium swept by radiation. Further development of the blast wave is strongly affected by the leading radiation front: the front plays the role of a precursor where the medium is loaded with e+- pairs and preaccelerated just ahead of the blast. It impacts the emission from the blast at R < R_{load}=5R_{gap} (the early afterglow). A spectacular observational effect results: GRB afterglows should start in optical/UV and evolve fast (< min) to a normal X-ray afterglow. The early optical emission observed in GRB 990123 may be explained in this way. The impact of the front is especially strong if the ambient medium is a wind from a massive progenitor of the GRB. In this case three phenomena are predicted: (1) The ejecta decelerates at R<R_{load} producing a lot of soft radiation. (2) The light curve of soft emission peaks at t_{peak}=40(1+z)E_{54}^{1/2}(Gamma_{ej}/100)^{-2} s where Gamma_{ej} is the Lorentz factor of the ejecta. Given measured redshift z and t_{peak}, one finds Gamma_{ej}. (3) The GRB acquires a spectral break at 5 - 50 MeV because harder photons are absorbed by radiation scattered in the wind.Comment: 20 pages, accepted to Ap

Evidence cross-validation and Bayesian inference of MAST plasma equilibria

In this paper, current profiles for plasma discharges on the Mega-Ampere Spherical Tokamak (MAST) are directly calculated from pickup coil, flux loop and Motional-Stark Effect (MSE) observations via methods based in the statistical theory of Bayesian analysis. By representing toroidal plasma current as a series of axisymmetric current beams with rectangular cross-section and inferring the current for each one of these beams, flux-surface geometry and q-profiles are subsequently calculated by elementary application of Biot-Savart's law. The use of this plasma model in the context of Bayesian analysis was pioneered by Svensson and Werner on the Joint-European Tokamak (JET) [J. Svensson and A. Werner. Current tomography for axisymmetric plasmas. Plasma Physics and Controlled Fusion, 50(8):085002, 2008]. In this framework, linear forward models are used to generate diagnostic predictions, and the probability distribution for the currents in the collection of plasma beams was subsequently calculated directly via application of Bayes' formula. In this work, we introduce a new diagnostic technique to identify and remove outlier observations associated with diagnostics falling out of calibration or suffering from an unidentified malfunction. These modifications enable good agreement between Bayesian inference of the last closed flux-surface (LCFS) with other corroborating data, such as such as that from force balance considerations using EFIT++ [L. Appel et al., Proc. 33rd EPS Conf., Rome, Italy, 2006]. In addition, this analysis also yields errors on the plasma current profile and flux-surface geometry, as well as directly predicting the Shafranov shift of the plasma core.This work was jointly funded by the Australian Government through International Science Linkages Grant No. CG130047, the Australian National University, the United Kingdom Engineering and Physical Sciences Research Council under Grant No. EP/G003955, and by the European Communities under the contract of Association between EURATOM and CCFE