Normal-incidence InAs self-assembled quantum-dot infrared photodetectors with a high detectivity

An InAs/AlGaAs quantum-dot infrared photodetector based on bound-to-bound intraband transitions in undoped InAs quantum dots is reported. AlGaAs blocking layers were employed to achieve low dark current. The photoresponse peaked at 6.2 /spl mu/m. At 77 K and -0.7 V bias, the responsivity was 14 mA/W and the detectivity, D*, was 10/sup 10/ cm/spl middot/Hz/sup 1/2//W.     

SAR-retrieved wind in polar regions-comparison with in situ data and atmospheric model output

European remote sensing (ERS) satellites synthetic aperture radar (SAR) wind retrievals using CMOD-IFR2 are, for the first time, retrieved in the marginal ice zone (MIZ) and in Arctic coastal areas and compared with in situ observations from reseach vessels (RVs) and output from a high-resolution atmospheric model. The root mean squares (rms) of the comparisons were 1.6 m s/sup -1/ and 2 m s/sup -1/, respectively. The spatial variation of the SAR wind fields established a decrease in wind speed close to the ice edge for the late summer situations where the wind was along the ice edge with the ice to the left. This decrease is believed to be due to changes in atmospheric stability, possibly through development of an internal boundary layer caused by the cold ice cover and melt water. Lower wind speed near the ice edge is confirmed by the atmospheric model and the in situ observations. Furthermore, good results are obtained from SAR wind retrieval in leads when compared with model output during a cold-air outbreak. Routine measurements in the MIZ are useful for estimating the wind stress, and therefore SAR may play an important role in this region. Finally, the identification of a jet out from Hinlopen Strait in the Svalbard region and low wind wakes along the coast in the SAR-retrieved wind field is confirmed by in situ observations as the RV moves through the region. The jet is also confirmed by the atmospheric model, which is able to reproduce the situation.     

Flicker noise in gate overlapped polycrystalline silicon thin-filmtransistors

A study of the noise performance of gate overlapped polycrystalline silicon thin-film transistors (TFTs) is presented. Low-frequency noise measurements were carried out on n- and p-type samples fabricated by excimer laser crystallization. It is shown that the carrier number fluctuation model applies not only to n-type but also to p-type devices. The density of oxide traps was extracted from the noise measurements and was of the order of 10¹⁸-10¹⁹ eV^-1 cm^-3     

Supermodularity and subadditivity properties of the entropy on themajorization lattice

We prove that the entropy is a supermodular and subadditive function on the lattice of all n-dimensional probability distributions, ordered according to the partial order relation defined by majorization among vectors     

Genetic Identification of Lophius budegassa and L. piscatorius by PCR-RFLP Analysis of a Mitochondrial tRNAGlu/Cytochrome bSegment

Identification of Lophius budegassa(black‐bellied angler) and L. piscatorius(angler) (Lophiiformes) was carried out on the amplification of a 486 bp tRNA^Glu/cytochrome b segment using the polymerase chain reaction (PCR). Direct DNA sequencing of 6 PCR products was carried out. Six restriction endonucleases (AluI, CfoI, HaeIII, HinfI, Mae, and ScrFI) with different species‐specific restriction fragment length polymorphism (RFLP) were selected. Digestions of PCR products from 30 individuals showed no intraspecific polymorphism. Double digestions (CfoI and HinfI, and HaeIII and ScrFI) were simpler and more rapid than single digestions. This technique is suitable for distinguishing tails of both Lophius species.     

Accuracy of approximations in MOSFET charge models

This paper analyzes the results of common approximations made in MOSFET charge modeling. The basis for the comparison is a charge-sheet model that is valid in all regions of operation. We show that proper modeling of surface potential as a function of position along the channel is more important for capacitance coefficient modeling accuracy than partitioning of inversion charge between source and drain. In addition, we show that there is a numerical error in previous charge-sheet formulations, and provide a solution for this problem     

An easy-to-use mismatch model for the MOS transistor

In this paper, a physics-based mismatch model is presented. It is demonstrated on a 0.18-/spl mu/m technology that a simple mismatch model can still be used to characterize deep-submicron technologies. The accuracy of the model is examined and found to be within 20% in the strong inversion region. Bulk bias dependence is modeled in a physical way. To extract the mismatch parameters, a weighted fit is introduced. It is shown that the width and length dependence of the mismatch parameters is given by the Pelgrom model.     

A PAC-Bayesian margin bound for linear classifiers

We present a bound on the generalization error of linear classifiers in terms of a refined margin quantity on the training sample. The result is obtained in a probably approximately correct (PAC)-Bayesian framework and is based on geometrical arguments in the space of linear classifiers. The new bound constitutes an exponential improvement of the so far tightest margin bound, which was developed in the luckiness framework, and scales logarithmically in the inverse margin. Even in the case of less training examples than input dimensions sufficiently large margins lead to nontrivial bound values and-for maximum margins-to a vanishing complexity term. In contrast to previous results, however, the new bound does depend on the dimensionality of feature space. The analysis shows that the classical margin is too coarse a measure for the essential quantity that controls the generalization error: the fraction of hypothesis space consistent with the training sample. The practical relevance of the result lies in the fact that the well-known support vector machine is optimal with respect to the new bound only if the feature vectors in the training sample are all of the same length. As a consequence, we recommend to use support vector machines (SVMs) on normalized feature vectors only. Numerical simulations support this recommendation and demonstrate that the new error bound can be used for the purpose of model selection.