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Using a tight-binding mode-space NEGF technique, we explore the essential physics, design and performance potential of the III-V core-shell (CS) nanowire (NW) heterojunction tunneling field-effect transistor (TFET). The CS TFET “line-tunneling” current increases significantly with the core diameter dC and outperforms the best III-V axial “point-tunneling” NW heterojunction TFET ION by up to 6× for dC = 6.6 nm. Reaching such a high level of current at low supply voltage, however, requires and involves specific and sometime unanticipated optimizations and physics that are thoroughly investigated here. In spite of the commonly accepted view, we also show and explain the weak gate-length dependency observed for the line-tunneling current in a III-V TFET. We further investigate the effect of electron-phonon scattering and discrete dopant impurity band tails on optimized CS NW TFETs. Including those non-idealities, the CS-TFET inverter performance significantly outperforms that of the axial TFETs. The low-power (LP) VDD = 0.35V CS-inverter delay is comparable to that of the high-performance (HP) Si CMOS using VDD = 0.55, which shows promise for an LP TFET technology with HP speed.

The electrical characteristics of n/p Ge nanowire transistors (NWTs) with the cross section of $6\times 6\mathrm{n}\mathrm{m}^{2}$ have been studied. The ION performance and the subthreshold swing are simulated by multi-subband Boltzmann transport equation and ballistic quantum transport solvers, respectively. The performance of nGe NWTs is sensitive to the barrier height of interfacial layer due to highly-anisotropic $\Lambda$-valleys. The dimension-dependent k·p parameters based on tight-binding full band are used to address the strong confinement of pGe NWTs. Comparing to Si NWTs, the intrinsic I ON is twice as high for both n/p Ge NWTs at 28nm channel length. As the channel length is scaled down, such ION benefit is maintained till the tunneling effect comes in and degrades the subthreshold swing.

This paper proposes a lightweight Bayesian calibration of dynamic models that accounts for model parameter uncertainties. A regression model was built to represent the dynamic model using parameter screening and multiple linear regression. Given this regression model and prior probability distributions of its input parameters, a Bayesian calibration method is developed to provide their posterior distributions. A case study is presented and the result shows considerable alignment between model prediction and measurement after calibration. This indicates its capability to perform fast risk-conscious calibration for most current retrofit practice where only monthly consumption and demand data are available.

This paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.

Maize, the highest-yielding cereal crop worldwide, is particularly susceptible to drought during its 2- to 3-week flowering period. Many genetic engineering strategies for drought tolerance impinge on plant development, reduce maximum yield potential or do not translate from laboratory conditions to the field. We overexpressed a gene encoding a rice trehalose-6-phosphate phosphatase (TPP) in developing maize ears using a floral promoter. This reduced the concentration of trehalose-6-phosphate (T6P), a sugar signal that regulates growth and development, and increased the concentration of sucrose in ear spikelets. Overexpression of TPP increased both kernel set and harvest index. Field data at several sites and over multiple seasons showed that the engineered trait improved yields from 9% to 49% under non-drought or mild drought conditions, and from 31% to 123% under more severe drought conditions, relative to yields from nontransgenic controls.&nbsp

This work investigates the formation mechanism of stress memorization technique (SMT)-induced edge dislocations and stacking faults during solid-phase epitaxy regrowth (SPER) using molecular dynamics (MD) simulation. During the SPER process of a patterned amorphous Si under a high-tensile capping film, growth fronts along the (1 1 0) and (0 0 1) planes collapse to form 5- and 7-rings which trigger the Frankel partial dislocation in the {1 1 1} plane. In addition, the line defects of stacking faults along {1 1 1} plane are generated with two symmetric boundaries of atomic structures which are confirmed as micro-twin defects. The MD simulation results are validated using high-resolution transmission electron microscopy and inverse fast Fourier transform images. The strain distribution obtained from the atomic structure reveals that the stress field is mainly caused by Frankel partial dislocations and the minor stress effect from the micro-twin defects.

Kennedy and O'Hagan (2001) propose a model for calibrating some unknown parameters in a computer model and estimating the discrepancy between the computer output and physical response. This model is known to have certain identifiability issues. Tuo and Wu (2016) show that there are examples for which the Kennedy-O'Hagan method renders unreasonable results in calibration. In spite of its unstable performance in calibration, the Kennedy-O'Hagan approach has a more robust behavior in predicting the physical response. In this work, we present some theoretical analysis to show the consistency of predictor based on their calibration model in the context of radial basis functions.

Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict for a range of untried points simultaneously. In this work we obtain some error bounds for the (simple) kriging predictor under the uniform metric. It works for a scattered set of input points in an arbitrary dimension, and also covers the case where the covariance function of the Gaussian process is misspecified. These results lead to a better understanding of the rate of convergence of kriging under the Gaussian or the Mat\'ern correlation functions, the relationship between space-filling designs and kriging models, and the robustness of the Mat\'ern correlation functions.

Thicker Al seed layer can enhance EM lifetime better.Thicker Al seed layer can improve SM performance better.Thicker Al seed layer brings more SM negative resistance shift. Display Omitted Effect of alloy seed on electromigration (EM) and stress migration (SM) has been analyzed based on Cu-based, dual-damascene technologies. It is found that the thickness of aluminium (Al) seed layer has strong impact on metal resistance, EM lifetime and SM performance. Thicker Al seed layer can induce higher metal resistance, improve EM lifetime better and bring more apparent negative shift of resistance during SM.

In current practice, HVAC systems are sized based on standardized procedures that were mostly developed by ASHRAE. The standard approach only implicitly deals with uncertainty in peak system demand through the selection of an appropriate design day and the choice of a safety factor. Although this method works satisfactorily in most cases, it offers no support to a system designer who wants to track the risk associated with an undersized system. The opposite, i.e. avoiding that the system is needlessly oversized deserves even more attention given the fact that current practice of “defensive sizing” leads to oversized systems which leads to wasted capital investment and systems that operate far away from the optimum efficiency loads. This paper explores a new framework to guide the use of uncertainty analysis (UA) and sensitivity analysis (SA) in HVAC system sizing. UA will replace the safety factor with quantified margins based on comprehensive quantification of different sources of uncertainty. A probabilistic-based SA is then used to identify the important individual factors or groups of factors that contribute to uncertainty, providing means of risk management by applying better quality assurance methods or negotiating performance contracts.

Traditional uncertainty quantification (UQ) in the prediction of building energy consumption has been limited to the propagation of uncertainties in model input parameters. Models by definition ignore, at least to some degree, and, in almost all cases, simplify the physical processes that govern the reality of interest, thereby introducing additional uncertainty in model predictions that cannot be captured as input parameter uncertainty. Quantification of this type of uncertainty (which we will refer to as model form uncertainty) is a necessary step towards the complete UQ of model predictions. This paper introduces a general framework for model form UQ and shows its application to the widely used sky irradiation model developed by Perez et al. [1990. “Modeling Daylight Availability and Irradiance Components from Direct and Global Irradiance.” Solar Energy 44 (5): 271–289], which computes solar diffuse irradiation on inclined surfaces. We collected a data set of one-year measurements of solar irradiation ...

In sensitivity testing the test specimens are subjected to a variety of stress levels to generate response or nonresponse. These data are used to estimate the critical stimulus (or threshold) level of the experimental object. Because of its versatile applications, several sensitivity testing procedures have been proposed and used in practice. There remains the outstanding question of finding an efficient procedure, especially when the sample size is small and the interest lies in the extreme percentiles. In the paper we propose a novel three-phase procedure, dubbed 3pod, which can be described as a trilogy of “search-estimate-approximate”. A core novel idea is to choose the stress levels to quickly achieve an overlapping data pattern which ensures the estimability of the underlying parameters. Simulation comparisons show that 3pod outperforms existing procedures over a range of scenarios in terms of efficiency and robustness.

Via impact on the upstream electromigration (EM) of low-k Cu interconnects was investigated based on 40/45nm technology node. It is found that via height is the main factor to affect EM performance. Increasing via height can lead to more early failure. Besides, alloy seed layer and argon power during via deposition are also observed to be two important factors to affect EM lifetime.

Kriging is commonly used for developing emulators as surrogates for computationally intensive simulations. One difficulty with kriging is the potential numerical instability in the computation of the inverse of the covariance matrix, which can lead to large variability and poor performance of the kriging predictor. First, we study some causes of ill-conditioning in kriging. We then study the use of nugget in kriging to overcome the numerical instability. Some asymptotic results on its interpolation bias and mean squared prediction errors are presented. Finally, we study the choice of the nugget parameter based on some algebraic lower bounds and use of a regularizing trace. A simulation study is performed to show the differences between kriging with and without nugget and to demonstrate the advantages of the former. This article has supplementary materials online.

Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new framework based on a hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified expectation–maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor–ligand bond lifetimes and waiting times which are essential in kinetic parameter estimation. Supplementary materials for this article are available online.

Various voltage bias and temperature stress conditions are adopted for hot carrier injection (HCI) test by not only the different device type (N or PMOS) but also the different technology with various device feature sizes (such as W/L=10/0.28µm and 10/0.1µm). Due to different dominant mechanism for hot carrier generation and performance degradation corresponding to various devices, the appropriate stress condition selection is crucial to address the HCI lifetime for device. Based on the conditions that may occur in real application and according to the experimental data, this work discussed the worst stress condition including the voltage and temperature for NMOS device with specific device feature size. Theoretical explanation is presented for each case. An HCI stress condition guideline is summarized in the end.

Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments., Comment: 49 pages, 4 figures

The last decade has seen a surge in the need for uncertainty analysis (UA) for building energy assessment. The rigorous determination of uncertainty in model parameters is a vital but often overlooked part of UA. To undertake this, one has to turn one's attention to a thriving area in engineering statistics that focuses on uncertainty quantification (UQ) for short. This paper applies dedicated methods and theories that are emerging in this area of statistics to the field of building energy models, and specifically to the microclimate variables embedded in them. We argue that knowing the uncertainty in these variables is a vital prerequisite for ensuing UA of whole building behaviour. Indeed, significant discrepancies have been observed between the predicted and measured state variables of building microclimates. This paper uses a set of approaches from the growing UQ arsenal, mostly regression-based methods, to develop statistical models that quantify the uncertainties in the following most significant mi...

Hot Carrier Injection (HCI) is a critical reliability concern especially for NMOSFET in IC. The drain current (Id) shift of NMOS at high Vds (drain stress) and low Vgs (gate stress) conditions was investigated in this paper. We found that the drain current (Id @ Vgs = 0.62 V, Vds = 5. 5 V) increased versus time at the stress conditions of Vgs = 0.62 V, Vds = 5.5 V while Idsat degraded at high Vds and low Vgs stress conditions. The dominant mechanism for this phenomenon is that the positive-charged traps generated in the gate oxide near the drain region. The experiments for process improvement showed that the holes injection of NMOSFET can be reduced by the proper annealing temperature of SiN and LDD (light doped drain) doping process.

The worst case stress conditions for hot carrier degradation have been investigated in detail at the devices fabricated with CMOS technologies from 0.35µm to 45nm. In nMOSFETs with long channel devices (L>0.18µm), Vg at Ibmax and the lowest temperature is the worst case condition; For short channel devices (0.13 µm ≤ L ≤ 0.18μm), Vg @ Ibmax and Vg=Vd stress conditions result in comparable hot carrier damage; For the very short channel transistors (L

Selective assembly has traditionally been used to achieve tight specifications on the clearance of two mating parts. However, its applicability is not limited to this particular type of assembly. This article develops a generalized version of selective assembly called GSA. It is a powerful tool that can be used to improve the quality of assemblies of single units of different component types. Two variants of GSA are considered: Direct Selective Assembly (DSA) and Fixed Bin Selective Assembly (FBSA). The former is selective assembly using information from measurements on component characteristics directly, whereas the latter is selective assembly of components sorted into bins. For each variant, the problem of matching the N components of each type to give N assemblies that minimize quality cost is formulated as a linear integer program. The component matching problem for DSA is an axial multi-index assignment problem, whereas for FBSA, it is an axial multi-index transportation problem. Simulations are per...

An analysis is conducted of the variable search (VS) method of Dorian Shainin. Certain conditions are identified where the VS approach is less effective than traditional designs. Theoretical and simulation studies establish the importance of process knowledge and noise variation to successful use of the VS design.

We report the capability to simulate in a quantum-mechanical atomistic fashion record-large nanowire devices, featuring several hundred to millions of atoms and a diameter up to 18.2 nm. We have employed a tight-binding mode-space NEGF technique demonstrating by far the fastest (up to 10 000 × faster) but accurate (error

Out-of-plane displacement (warpage) has been a major thermomechanical reliability concern for board-level electronic packages. Printed wiring board (PWB) and component warpage results principally from coefficient of thermal expansion mismatch among the materials that make up the PWB assembly (PWBA). Warpage occurring during surface-mount assembly reflow processes and normal operations may lead to severe solder bump reliability problems. In this research, the effect of initial PWB warpage on the low cycle thermal fatigue reliability of the solder bumps in plastic ball grid array (PBGA) packages was studied using experimental and analytical methods. A real-time projection moire? warpage measurement system was used to measure the surface topology of PWBA samples at different temperatures. The thermal fatigue reliability of solder bumps was evaluated from experimental thermal cycling tests and finite element simulation results. Three-dimensional (3-D) models of PWBAs with varying board warpage were used to estimate the solder bump fatigue life for different types of PBGAs mounted on PWBs. In order to improve the accuracy of FE results, the projection moire? method was used to measure the initial warpage of PWBs, and this warpage was used as a geometric input to the finite element method. The simulation results were validated and correlated with the experimental results obtained using the projection moire? technique and accelerated thermal cycling tests. An advanced prediction model was generated to predict board level solder bump fatigue life based on the initial PWB warpage, package dimensions and locations, and solder bump materials.

Controlling the morphology of the as-synthesized nanostructures is usually challenging, and there lacks of a general theoretical guidance in experimental approach. In this study, a novel way of optimizing the aspect ratio of hydrothermally grown ZnO nanowire (NW) arrays is presented by utilizing a systematic statistical design and analysis method. In this work, we use pick-the-winner rule and one-pair-at-a-time main effect analysis to sequentially design the experiments and identify optimal reaction settings. By controlling the hydrothermal reaction parameters (reaction temperature, time, precursor concentration, and capping agent), we improved the aspect ratio of ZnO NWs from around 10 to nearly 23. The effect of noise on the experimental results was identified and successfully reduced, and the statistical design and analysis methods were very effective in reducing the number of experiments performed and in identifying the optimal experimental settings. In addition, the antireflection spectrum of the as-synthesized ZnO NWs clearly shows that higher aspect ratio of the ZnO NW arrays leads to about 30% stronger suppression in the UV-vis range emission. This shows great potential applications as antireflective coating layers in photovoltaic devices.

Motivated by a Bayesian framework, we propose a new minimum aberration-type criterion for designing experiments with two- and four-level factors. The Bayesian approach helps in overcoming the ad hoc nature of effect ordering in the existing minimum aberration-type criteria. The approach is also capable of distinguishing between qualitative and quantitative factors. Numerous examples are given to demonstrate its advantages. Copyright 2009, Oxford University Press.

We propose an approach to constructing a new type of design, a sliced space-filling design, intended for computer experiments with qualitative and quantitative factors. The approach starts with constructing a Latin hypercube design based on a special orthogonal array for the quantitative factors and then partitions the design into groups corresponding to different level combinations of the qualitative factors. The points in each group have good space-filling properties. Some illustrative examples are given. Copyright 2009, Oxford University Press.

We systematically investigate the best process conditions that ensure synthesis of different types of one-dimensional cadmium selenide nanostructures with high yield and reproducibility. Through a designed experiment and rigorous statistical analysis of experimental data, models linking the probabilities of obtaining specific morphologies to the process variables are developed. A new iterative algorithm for fitting a multinomial generalized linear model is proposed and used. The optimum process conditions, which maximize the preceding probabilities and make the synthesis process robust (i.e., less sensitive) to variations in process variables around set values, are derived from the fitted models using Monte Carlo simulations.Cadmium selenide has been found to exhibit one-dimensional morphologies of nanowires, nanobelts, and nanosaws, often with the three morphologies being intimately intermingled within the as-deposited material. A slight change in growth condition can result in a totally different morpho...

A special type of factor called an amplification factor is used in the failure amplification method (FAMe) to maximize the information in the experiment. A strategy for model building in FAMe is proposed using generalized linear models (GLM), and the b..

Repeated adhesion frequency assay is the only published method for measuring the kinetic rates of cell adhesion. Cell adhesion plays an important role in many physiological and pathological processes. Traditional analysis of adhesion frequency experiments assumes that the adhesion test cycles are independent Bernoulli trials. This assumption can often be violated in practice. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating random effects is developed in this paper. A goodness-of-fit statistic is introduced to assess the adequacy of distribution assumptions on the dependent binary data with random effects. The asymptotic distribution of the goodness-of-fit statistic is derived and its finite-sample performance is examined via a simulation study. Application of the proposed methodology to real data from a T-cell experiment reveals some interesting information, including the dependency between repeated adhesion tests.

Technometrics was founded in 1959 as a forum for publishing statistical methods and applications in engineering and the physical and chemical sciences. The expanding role of statistics in industry was a major stimulus, and, throughout the years many articles in the journal have been motivated by industrial problems. In this panel discussion we look ahead to the future of industrial statistics. Ten experts, encompassing a range of backgrounds, experience, and expertise, answered my request to share with us their thoughts on what lies ahead in industrial statistics. Short biographical sketches of the panelists are provided at the end of the discussion. The panelists wrote independent essays, which I have combined into an integrated discussion. Most of the essays were written as responses to a list of 10 questions that I provided to help the participants direct their thoughts. I have organized the discussion in that same fashion, stating the questions and then providing the related responses. Several discussants added remarks on the role of statistics journals, particularly of Technometrics, and I have added that as a final question. We see this article, not as the end of the story, but rather as the takeoff point for further discussion. To that end, we are initiating an open discussion forum; to participate, go to http://www.asq.org/pub/techno/ and click on Networking and Events. The American Society for Quality will host the forum and Bert Gunter has graciously agreed to serve as moderator.

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as {simplifying assumptions} for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations., Comment: Submitted to JASA A&CS

The Robbins-Monro stochastic approximation procedure has been used for sensitivity testing. Joseph (2004) recognized that it is not well suited for binary data and proposed a modification which gives better performance for p between 0.1 and 0.9. However, for extreme p values, say p ≤ 0.01 or p ≥ 0.99, the Joseph version does not perform well. To overcome this difficulty, we propose a modification based on an asymmetric quadratic loss function. The new procedure can speed up convergence by employing different penalties for undershooting and overshooting to reduce the expected loss. Simulation comparisons show the clear advantages of the new procedure for extreme p values.

This paper presents the fabrication, design and electrical performance of magnetic thin-film inductors for monolithic integration with CMOS for DC-DC power conversion. Magnetic core inductors were fabricated using conventional CMOS processes to achieve peak inductance density of 290nH/mm2, quality factor 15 at 150MHz, current density exceeding 11 A/mm2 and coupling coefficient of 0.89 for coupled inductors.

Many computer models contain unknown parameters which need to be estimated using physical observations. Tuo and Wu (2014) show that the calibration method based on Gaussian process models proposed by Kennedy and O’Hagan [J. R. Stat. Soc. Ser. B. Stat. Methodol. 63 (2001) 425–464] may lead to an unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the $L_{2}$ calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Numerical examples show that the proposed method outperforms the existing ones.

In summary, this work highlights the impact of SMT-induced edge-dislocation positions in nFET device design. Based on experimental results and atomic transport simulation, dislocations with reduced proximity and depth would increase the amount of SFs and TDs which induce high parasitic resistance and high I boff leakage current together. Trade-off among strained mobility, parasitic resistance and I boff should be made for advanced device design.

Two classes of designs of resolution V are constructed using one-factor-at-a-time techniques. They facilitate sequential learning and are more economical in run size than regular 2Vk−p designs. Comparisons of D efficiency with other designs are given to assess the suitability of the proposed designs. Their Ds efficiencies can be dramatically improved with the addition of a few runs.

In ASL, two-handed signs fall into three major sets. In one set the hands have different shapes and either only the dominant hand moves or the hands move as a unit. Battison’s Dominance Condition was intended to account for the fact that the non-dominant hand typically assumes an unmarked shape when it is stationary. However, we show that the non-dominant hand does this even when the hands move as a unit. In the second set the hands have the same shape and only the dominant hand moves. These signs are unrestricted for handshape. In the third set the hands have the same shape and both move. Battison’s Symmetry Condition was intended to account for restrictions on the parameters of these signs. We argue that four basic types of symmetry transformations occur, with various complications: reflection, rotation, translation, and glide reflection, all of which call for conditions specific to them, and lead to an overriding condition on movement in symmetry transformation signs. The conditions uncovered here might be morpheme structure constraints or, instead, simply follow from physiological limitations of hands in motion.