The activation energy for GaAs/AlGaAs interdiffusion

Copyright 1997 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. This article appeared in Journal of Applied Physics 82, 4842 (1997) and may be found at

Direct evidence for a piezoelectriclike effect in coherently strained SiGe/Si heterostructures

A hybrid acoustic spectroscopy technique has been used to demonstrate the (reversible) conversion of high frequency electric fields into longitudinal acoustic waves within a modulation-doped pseudomorphic Si/Si0.88Ge0.12/Si heterostructure. This provides compelling evidence for the existence of a piezoelectriclike coupling within such structures

Structure and energetics of the Si-SiO_2 interface

Silicon has long been synonymous with semiconductor technology. This unique role is due largely to the remarkable properties of the Si-SiO_2 interface, especially the (001)-oriented interface used in most devices. Although Si is crystalline and the oxide is amorphous, the interface is essentially perfect, with an extremely low density of dangling bonds or other electrically active defects. With the continual decrease of device size, the nanoscale structure of the silicon/oxide interface becomes more and more important. Yet despite its essential role, the atomic structure of this interface is still unclear. Using a novel Monte Carlo approach, we identify low-energy structures for the interface. The optimal structure found consists of Si-O-Si "bridges" ordered in a stripe pattern, with very low energy. This structure explains several puzzling experimental observations.Comment: LaTex file with 4 figures in GIF forma

Deep electronic states in ion-implanted Si

In this paper we present an overview of the deep states present after ion-implantation by various species into n-type silicon, measured by Deep Level Transient Spectroscopy (DLTS) and high resolution Laplace DLTS (LDLTS). Both point and small extended defects are found, prior to any anneal, which can therefore be the precursors to more detrimental defects such as end of range loops. We show that the ion mass is linked to the concentrations of defects that are observed, and the presence of small interstitial clusters directly after ion implantation is established by comparing their behaviour with that of electrically active stacking faults. Finally, future applications of the LDLTS technique to ion-implanted regions in Si-based devices are outlined.</p

Coherent diffraction of single Rice Dwarf virus particles using hard X-rays at the Linac Coherent Light Source

Single particle diffractive imaging data from Rice Dwarf Virus (RDV) were recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS). RDV was chosen as it is a wellcharacterized model system, useful for proof-of-principle experiments, system optimization and algorithm development. RDV, an icosahedral virus of about 70 nm in diameter, was aerosolized and injected into the approximately 0.1 mu m diameter focused hard X-ray beam at the CXI instrument of LCLS. Diffraction patterns from RDV with signal to 5.9 angstrom ngstrom were recorded. The diffraction data are available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development, the contents of which are described here.11Ysciescopu

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study.

Background: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18-36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth. Methods: Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is determined by reference to exactly known facts pertaining to each fetus-specifically, intervals between ultrasound visits-rather than the date of the mother's last menstrual period. The data stem from a sample of healthy, well-nourished participants in a large, multicentre, population-based study, the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). The generalisability of the algorithm is shown with data from a different and more heterogeneous population (INTERBIO-21st Fetal Study). Findings: In the context of two large datasets, we estimated gestational age between 20 and 30 weeks of gestation with 95% confidence to within 3 days, using measurements made in a 10-week window spanning the second and third trimesters. Fetal gestational age can thus be estimated in the 20-30 weeks gestational age window with a prediction interval 3-5 times better than with any previous algorithm. This will enable improved management of individual pregnancies. 6-week forecasts of the growth trajectory for a given fetus are accurate to within 7 days. This will help identify at-risk fetuses more accurately than currently possible. At population level, the higher accuracy is expected to improve fetal growth charts and population health assessments. Interpretation: Machine learning can circumvent long-standing limitations in determining fetal gestational age and future growth trajectory, without recourse to often inaccurately known information, such as the date of the mother's last menstrual period. Using this algorithm in clinical practice could facilitate the management of individual pregnancies and improve population-level health. Upon publication of this study, the algorithm for gestational age estimates will be provided for research purposes free of charge via a web portal. Funding: Bill & Melinda Gates Foundation, Office of Science (US Department of Energy), US National Science Foundation, and National Institute for Health Research Oxford Biomedical Research Centre

Localization-enhanced biexciton binding in semiconductors

The influence of excitonic localization on the binding energy of biexcitons is investigated for quasi-three-dimensional and quasi-two-dimensional AlxGa1−xAs structures. An increase of the biexciton binding energy is observed for localization energies comparable to or larger than the free biexciton binding energy. A simple analytical model for localization in the weak confinement regime ascribes the increase to a quenching of the additional kinetic energy of the exciton-exciton motion in the biexciton