arXiv subjects

Han Liu

Publications and source records attributed to Han Liu.

At least 415 records · Page 23Linked to original sources

arXiv2014

The Effect of Dielectric Capping on Few-Layer Phosphorene Transistors: Tuning the Schottky Barrier Heights

Phosphorene is a unique single elemental semiconductor with two-dimensional layered structures. In this letter, we study the transistor behavior on mechanically exfoliated few-layer phosphorene with the top-gate. We achieve a high on-current of 144 mA/mm and hole mobility of 95.6 cm2/Vs. We deposit Al2O3 by atomic layer deposition (ALD) and study the effects of dielectric capping. We observe that the polarity of the transistors alternated from p-type to ambipolar with Al2O3 grown on the top. We attribute this transition to the changes for the effective Schottky barrier heights for both electrons and holes at the metal contact edges, which is originated from fixed charges in the ALD dielectric.

cond-mat.mes-hall↗

arXiv2014

MoS2 Field-effect Transistors with Graphene/Metal Heterocontacts

For the first time, n-type few-layer MoS2 field-effect transistors with graphene/Ti as the hetero-contacts have been fabricated, showing more than 160 mA/mm drain current at 1 μm gate length with an on-off current ratio of 107. The enhanced electrical characteristic is confirmed in a nearly 2.1 times improvement in on-resistance and a 3.3 times improvement in contact resistance with hetero-contacts compared to the MoS2 FETs without graphene contact layer. Temperature dependent study on MoS2/graphene hetero-contacts has been also performed, still unveiling its Schottky contact nature. Transfer length method and a devised I-V method have been introduced to study the contact resistance and Schottky barrier height in MoS2/graphene /metal hetero-contacts structure.

cond-mat.mtrl-sci↗

arXiv2014

Molecular Doping of Multilayer MoS2 Field-effect Transistors: Reduction in Sheet and Contact Resistances

For the first time, polyethyleneimine (PEI) doping on multilayer MoS2 field-effect transistors are investigated. A 2.6 times reduction in sheet resistance, and 1.2 times reduction in contact resistance have been achieved. The enhanced electrical characteristics are also reflected in a 70% improvement in ON current, and 50% improvement in extrinsic field-effect mobility. The threshold voltage also confirms a negative shift upon the molecular doping. All studies demonstrate the feasibility of PEI molecular doping in MoS2 transistors, and its potential applications in layer-structured semiconducting 2D crystals.

cond-mat.mtrl-sci↗

arXiv2014

High Dimensional Semiparametric Latent Graphical Model for Mixed Data

Graphical models are commonly used tools for modeling multivariate random variables. While there exist many convenient multivariate distributions such as Gaussian distribution for continuous data, mixed data with the presence of discrete variables or a combination of both continuous and discrete variables poses new challenges in statistical modeling. In this paper, we propose a semiparametric model named latent Gaussian copula model for binary and mixed data. The observed binary data are assumed to be obtained by dichotomizing a latent variable satisfying the Gaussian copula distribution or the nonparanormal distribution. The latent Gaussian model with the assumption that the latent variables are multivariate Gaussian is a special case of the proposed model. A novel rank-based approach is proposed for both latent graph estimation and latent principal component analysis. Theoretically, the proposed methods achieve the same rates of convergence for both precision matrix estimation and eigenvector estimation, as if the latent variables were observed. Under similar conditions, the consistency of graph structure recovery and feature selection for leading eigenvectors is established. The performance of the proposed methods is numerically assessed through simulation studies, and the usage of our methods is illustrated by a genetic dataset.

stat.ML↗

arXiv2014

High Dimensional Semiparametric Scale-Invariant Principal Component Analysis

We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. COCA improves upon PCA and sparse PCA in three aspects: (i) It is robust to modeling assumptions; (ii) It is robust to outliers and data contamination; (iii) It is scale-invariant and yields more interpretable results. We prove that the COCA estimators obtain fast estimation rates and are feature selection consistent when the dimension is nearly exponentially large relative to the sample size. Careful experiments confirm that COCA outperforms sparse PCA on both synthetic and real-world datasets.

stat.ML↗

arXiv2014

Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning

Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics. However, existing methods for parameter estimation, inference, and structure learning mainly rely on the Gaussian or discrete assumptions, which are restrictive under many applications. In this paper, we propose new nonparametric methods based on reproducing kernel Hilbert space embeddings of distributions that can recover the latent tree structures, estimate the parameters, and perform inference for high dimensional continuous and non-Gaussian variables. The usefulness of the proposed methods are illustrated by thorough numerical results.

stat.ML↗

arXiv2014

Phosphorene: A New 2D Material with High Carrier Mobility

Preceding the current interest in layered materials for electronic applications, research in the 1960's found that black phosphorus combines high carrier mobility with a fundamental band gap. We introduce its counterpart, dubbed few-layer phosphorene, as a new 2D p-type material. Same as graphene and MoS2, phosphorene is flexible and can be mechanically exfoliated. We find phosphorene to be stable and, unlike graphene, to have an inherent, direct and appreciable band-gap that depends on the number of layers. Our transport studies indicate a carrier mobility that reflects its structural anisotropy and is superior to MoS2. At room temperature, our phosphorene field-effect transistors with 1.0 um channel length display a high on-current of 194 mA/mm, a high hole field-effect mobility of 286 cm2/Vs, and an on/off ratio up to 1E4. We demonstrate the possibility of phosphorene integration by constructing the first 2D CMOS inverter of phosphorene PMOS and MoS2 NMOS transistors.

cond-mat.mes-hall↗

arXiv2013

Switching Mechanism in Single-Layer Molybdenum Disulfide Transistors: an Insight into Current Flow across Schottky Barriers

In this article, we study the properties of metal contacts to single-layer molybdenum disulfide (MoS2) crystals, revealing the nature of switching mechanism in MoS2 transistors. On investigating transistor behavior as contact length changes, we find that the contact resistivity for metal/MoS2 junctions is defined by contact area instead of contact width. The minimum gate dependent transfer length is ~0.63 μm in the on-state for metal (Ti) contacted single-layer MoS2. These results reveal that MoS2 transistors are Schottky barrier transistors, where the on/off states are switched by the tuning the Schottky barriers at contacts. The effective barrier heights for source and drain barriers are primarily controlled by gate and drain biases, respectively. We discuss the drain induced barrier narrowing effect for short channel devices, which may reduce the influence of large contact resistance for MoS2 Schottky barrier transistors at the channel length scaling limit.

cond-mat.mes-hall↗

arXiv2013

Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification

In this paper we present two new approaches to efficiently solve large-scale compressed sensing problems. These two ideas are independent of each other and can therefore be used either separately or together. We consider all possibilities. For the first approach, we note that the zero vector can be taken as the initial basic (infeasible) solution for the linear programming problem and therefore, if the true signal is very sparse, some variants of the simplex method can be expected to take only a small number of pivots to arrive at a solution. We implemented one such variant and demonstrate a dramatic improvement in computation time on very sparse signals. The second approach requires a redesigned sensing mechanism in which the vector signal is stacked into a matrix. This allows us to exploit the Kronecker compressed sensing (KCS) mechanism. We show that the Kronecker sensing requires stronger conditions for perfect recovery compared to the original vector problem. However, the Kronecker sensing, modeled correctly, is a much sparser linear optimization problem. Hence, algorithms that benefit from sparse problem representation, such as interior-point methods, can solve the Kronecker sensing problems much faster than the corresponding vector problem. In our numerical studies, we demonstrate a ten-fold improvement in the computation time.

stat.ML↗

arXiv2013

Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model

In this manuscript a unified framework for conducting inference on complex aggregated data in high dimensional settings is proposed. The data are assumed to be a collection of multiple non-Gaussian realizations with underlying undirected graphical structures. Utilizing the concept of median graphs in summarizing the commonality across these graphical structures, a novel semiparametric approach to modeling such complex aggregated data is provided along with robust estimation of the median graph, which is assumed to be sparse. The estimator is proved to be consistent in graph recovery and an upper bound on the rate of convergence is given. Experiments on both synthetic and real datasets are conducted to illustrate the empirical usefulness of the proposed models and methods.

stat.AP↗

arXiv2013

Magneto-Transport in MoS2: Phase Coherence, Spin Orbit Scattering and the Hall Factor

We have characterized phase coherence length, spin orbit scattering length, and the Hall factor in n-type MoS2 2D crystals via weak localization measurements and Hall-effect measurements. Weak localization measurements reveal a phase coherence length of ~50 nm at T = 400 mK for a few-layer MoS2 film, decreasing as T^-1/2 with increased temperatures. Weak localization measurements also allow us, for the first time without optical techniques, to estimate the spin orbit scattering length to be 430 nm, pointing to the potential of MoS2 for spintronics applications. Via Hall-effect measurements, we observe a low temperature Hall mobility of 311 cm2/Vs at T = 1 K which decreases as a power law with a characteristic exponent γ=1.5 from 10 K to 60 K. At room temperature, we observe Hall mobility of 24 cm2/Vs. By determining the Hall factor for MoS2 to be 1.35 at T = 1 K and 2.4 at room temperature, we observe drift mobility of 420 cm2/Vs and 56 cm2/Vs at T = 1 K and room temperature, respectively.

cond-mat.mes-hall↗

arXiv2013

Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models

We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension $d$ to scale with the series length $T$. We treat the transition matrix of time series as a nuisance parameter and directly apply sparse principal component analysis on multivariate time series as if the data are independent. We provide explicit non-asymptotic rates of convergence for leading eigenvector estimation and extend this result to principal subspace estimation. Our analysis illustrates that the spectral norm of the transition matrix plays an essential role in determining the final rates. We also characterize sufficient conditions under which sparse principal component analysis attains the optimal parametric rate. Our theoretical results are backed up by thorough numerical studies.

stat.ML↗

arXiv2013

Optimal Feature Selection in High-Dimensional Discriminant Analysis

We consider the high-dimensional discriminant analysis problem. For this problem, different methods have been proposed and justified by establishing exact convergence rates for the classification risk, as well as the l2 convergence results to the discriminative rule. However, sharp theoretical analysis for the variable selection performance of these procedures have not been established, even though model interpretation is of fundamental importance in scientific data analysis. This paper bridges the gap by providing sharp sufficient conditions for consistent variable selection using the sparse discriminant analysis (Mai et al., 2012). Through careful analysis, we establish rates of convergence that are significantly faster than the best known results and admit an optimal scaling of the sample size n, dimensionality p, and sparsity level s in the high-dimensional setting. Sufficient conditions are complemented by the necessary information theoretic limits on the variable selection problem in the context of high-dimensional discriminant analysis. Exploiting a numerical equivalence result, our method also establish the optimal results for the ROAD estimator (Fan et al., 2012) and the sparse optimal scaling estimator (Clemmensen et al., 2011). Furthermore, we analyze an exhaustive search procedure, whose performance serves as a benchmark, and show that it is variable selection consistent under weaker conditions. Extensive simulations demonstrating the sharpness of the bounds are also provided.

stat.ML↗

arXiv2013

Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions

This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images is acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify and quantify lesion enhancement location at the subject level and lesion enhancement patterns at the population level. With this example, we aim to address the difficult problem of transforming a qualitative scientific null hypothesis, such as "this voxel does not enhance", to a well-defined and numerically testable null hypothesis based on existing data. We call the procedure "soft null hypothesis" testing as opposed to the standard "hard null hypothesis" testing. This problem is fundamentally different from: 1) testing when a quantitative null hypothesis is given; 2) clustering using a mixture distribution; or 3) identifying a reasonable threshold with a parametric null assumption. We analyze a total of 20 subjects scanned at 63 visits (~30Gb), the largest population of such clinical brain images.

stat.AP↗

arXiv2013

Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming

We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such sub-populations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves a novel approach, in which we first transform the original multiple testing problem into a large, sparse linear program. We then solve this problem using advanced optimization techniques. This general method can solve a variety of multiple testing problems and decision theory problems related to optimal trial design, for which no solution was previously available. In particular, we construct new multiple testing procedures that satisfy minimax and Bayes optimality criteria. For a given optimality criterion, our new approach yields the optimal tradeoff? between power to detect an effect in the overall population versus power to detect effects in subpopulations. We demonstrate our approach in examples motivated by two randomized trials of new treatments for HIV.

stat.ME↗

arXiv2013

Graph Estimation From Multi-attribute Data

Many real world network problems often concern multivariate nodal attributes such as image, textual, and multi-view feature vectors on nodes, rather than simple univariate nodal attributes. The existing graph estimation methods built on Gaussian graphical models and covariance selection algorithms can not handle such data, neither can the theories developed around such methods be directly applied. In this paper, we propose a new principled framework for estimating graphs from multi-attribute data. Instead of estimating the partial correlation as in current literature, our method estimates the partial canonical correlations that naturally accommodate complex nodal features. Computationally, we provide an efficient algorithm which utilizes the multi-attribute structure. Theoretically, we provide sufficient conditions which guarantee consistent graph recovery. Extensive simulation studies demonstrate performance of our method under various conditions. Furthermore, we provide illustrative applications to uncovering gene regulatory networks from gene and protein profiles, and uncovering brain connectivity graph from functional magnetic resonance imaging data.

stat.ML↗

arXiv2013

Statistical Study of Deep Sub-Micron Dual-Gated Field-Effect Transistors on Monolayer CVD Molybdenum Disulfide Films

Monolayer Molybdenum Disulfide (MoS2) with a direct band gap of 1.8 eV is a promising two-dimensional material with a potential to surpass graphene in next generation nanoelectronic applications. In this letter, we synthesize monolayer MoS2 on Si/SiO2 substrate via chemical vapor deposition (CVD) method and comprehensively study the device performance based on dual-gated MoS2 field-effect transistors. Over 100 devices are studied to obtain a statistical description of device performance in CVD MoS2. We examine and scale down the channel length of the transistors to 100 nm and achieve record high drain current of 62.5 mA/mm in CVD monolayer MoS2 film ever reported. We further extract the intrinsic contact resistance of low work function metal Ti on monolayer CVD MoS2 with an expectation value of 175 Ω.mm, which can be significantly decreased to 10 Ω.mm by appropriate gating. Finally, field-effect mobilities (μFE) of the carriers at various channel lengths are obtained. By taking the impact of contact resistance into account, an average and maximum intrinsic μFE is estimated to be 13.0 and 21.6 cm2/Vs in monolayer CVD MoS2 films, respectively.

cond-mat.mes-hall↗

arXiv2013

Sparse Nonparametric Graphical Models

We present some nonparametric methods for graphical modeling. In the discrete case, where the data are binary or drawn from a finite alphabet, Markov random fields are already essentially nonparametric, since the cliques can take only a finite number of values. Continuous data are different. The Gaussian graphical model is the standard parametric model for continuous data, but it makes distributional assumptions that are often unrealistic. We discuss two approaches to building more flexible graphical models. One allows arbitrary graphs and a nonparametric extension of the Gaussian; the other uses kernel density estimation and restricts the graphs to trees and forests. Examples of both methods are presented. We also discuss possible future research directions for nonparametric graphical modeling.

stat.ML↗