arXiv subjects

Han Liu

Publications and source records attributed to Han Liu.

431 records · Page 24Linked to original sources

arXiv2012

Channel Length Scaling of MoS2 MOSFETs

In this article, we investigate electrical transport properties in ultrathin body (UTB) MoS2 two-dimensional (2D) crystals with channel lengths ranging from 2 μm down to 50 nm. We compare the short channel behavior of sets of MOSFETs with various channel thickness, and reveal the superior immunity to short channel effects of MoS2 transistors. We observe no obvious short channel effects on the device with 100 nm channel length (Lch) fabricated on a 5 nm thick MoS2 2D crystal even when using 300 nm thick SiO2 as gate dielectric, and has a current on/off ratio up to ~109. We also observe the on-current saturation at short channel devices with continuous scaling due to the carrier velocity saturation. Also, we reveal the performance limit of short channel MoS2 transistors is dominated by the large contact resistance from the Schottky barrier between Ni and MoS2 interface, where a fully transparent contact is needed to achieve a high-performance short channel device.

cond-mat.mtrl-sci↗

arXiv2012

TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models

We propose a new procedure for estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: it requires very few efforts to choose the tuning parameter in finite sample settings. Computationally, our procedure is significantly faster than existing methods due to its tuning-insensitive property. Theoretically, the obtained estimator is simultaneously minimax optimal for precision matrix estimation under different norms. Empirically, we illustrate the advantages of our method using thorough simulated and real examples. The R package bigmatrix implementing the proposed methods is available on the Comprehensive R Archive Network: http://cran.r-project.org/.

stat.ME↗

arXiv2012

High Dimensional Semiparametric Gaussian Copula Graphical Models

In this paper, we propose a semiparametric approach, named nonparanormal skeptic, for efficiently and robustly estimating high dimensional undirected graphical models. To achieve modeling flexibility, we consider Gaussian Copula graphical models (or the nonparanormal) as proposed by Liu et al. (2009). To achieve estimation robustness, we exploit nonparametric rank-based correlation coefficient estimators, including Spearman's rho and Kendall's tau. In high dimensional settings, we prove that the nonparanormal skeptic achieves the optimal parametric rate of convergence in both graph and parameter estimation. This celebrating result suggests that the Gaussian copula graphical models can be used as a safe replacement of the popular Gaussian graphical models, even when the data are truly Gaussian. Besides theoretical analysis, we also conduct thorough numerical simulations to compare different estimators for their graph recovery performance under both ideal and noisy settings. The proposed methods are then applied on a large-scale genomic dataset to illustrate their empirical usefulness. The R language software package huge implementing the proposed methods is available on the Comprehensive R Archive Network: http://cran. r-project.org/.

stat.ML↗

arXiv2012

The Nonparanormal SKEPTIC

We propose a semiparametric approach, named nonparanormal skeptic, for estimating high dimensional undirected graphical models. In terms of modeling, we consider the nonparanormal family proposed by Liu et al (2009). In terms of estimation, we exploit nonparametric rank-based correlation coefficient estimators including the Spearman's rho and Kendall's tau. In high dimensional settings, we prove that the nonparanormal skeptic achieves the optimal parametric rate of convergence in both graph and parameter estimation. This result suggests that the nonparanormal graphical models are a safe replacement of the Gaussian graphical models, even when the data are Gaussian.

stat.ME↗

arXiv2012

MoS2 Nanoribbon Transistors: Transition from Depletion-mode to Enhancement-mode by Channel Width Trimming

We study the channel width scaling of back-gated MoS2 metal-oxide-semiconductor field-effect transistors (MOSFETs) from 2 μm down to 60 nm. We reveal that the channel conductance scales linearly with channel width, indicating no evident edge damage for MoS2 nanoribbons with widths down to 60 nm as defined by plasma dry etching. However, these transistors show a strong positive threshold voltage (VT) shift with narrow channel widths of less than 200 nm. Our results also show that transistors with thinner channel thicknesses have larger VT shifts associated with width scaling. Devices fabricated on a 6 nm thick MoS2 crystal underwent the transition from depletion-mode to enhancement-mode.

cond-mat.mes-hall↗

arXiv2012

The Integration of High-k Dielectric on Two-Dimensional Crystals by Atomic Layer Deposition

We investigate the integration of Al2O3 high-k dielectric on two-dimensional (2D) crystals of boron nitride (BN) and molybdenum disulfide (MoS2) by atomic layer deposition (ALD). We demonstrate the feasibility of direct ALD growth with trimethylaluminum(TMA) and water as precursors on both 2D crystals. Through theoretical and experimental studies, we found that the initial ALD cycles play the critical role, during which physical adsorption dominates precursor adsorption at the semiconductor surface. We model the initial ALD growth stages at the 2D surface by analyzing Lennard-Jones Potentials, which could guide future optimization of the ALD process on 2D crystals.

cond-mat.mes-hall↗

arXiv2011

MoS2 Dual-Gate MOSFET with Atomic-Layer-Deposited Al2O3 as Top-Gate Dielectric

We demonstrate atomic-layer-deposited (ALD) high-k dielectric integration on two-dimensional (2D) layer-structured molybdenum disulfide (MoS2) crystals and MoS2 dual-gate n-channel MOSFETs with ALD Al2O3 as top-gate dielectric. Our C-V study of MOSFET structures shows good interface between 2D MoS2 crystal and ALD Al2O3. Maximum drain currents using back-gates and top-gates are measured to be 7.07mA/mm and 6.42mA/mm at Vds=2V with a channel width of 3 μm, a channel length of 9 μm, and a top-gate length of 3 μm. We achieve the highest field-effect mobility of electrons using back-gate control to be 517 cm^2/Vs. The highest current on/off ratio is over 10^8.

cond-mat.mtrl-sci↗

arXiv2011

Atomic-Layer-Deposited Al2O3 on Bi2Te3 for Topological Insulator Field-Effect Transistors

We report dual-gate modulation of topological insulator field-effect transistors (TI FETs) made on Bi2Te3 thin flakes with integration of atomic-layer-deposited (ALD) Al2O3 high-k dielectric. Atomic force microscopy study shows that ALD Al2O3 is uniformly grown on this layer-structured channel material. Electrical characterization reveals that the right selection of ALD precursors and the related surface chemistry play a critical role in device performance of Bi2Te3 based TI FETs. We realize both top-gate and bottom-gate control on these devices, and the highest modulation rate of 76.1% is achieved by using simultaneous dual gate control.

cond-mat.mtrl-sci↗

arXiv2011

Compressive Network Analysis

Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets.

stat.ML↗

arXiv2010

Forest Density Estimation

We study graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical models. For density estimation, we do not assume the true distribution corresponds to a forest; rather, we form kernel density estimates of the bivariate and univariate marginals, and apply Kruskal's algorithm to estimate the optimal forest on held out data. We prove an oracle inequality on the excess risk of the resulting estimator relative to the risk of the best forest. For graph estimation, we consider the problem of estimating forests with restricted tree sizes. We prove that finding a maximum weight spanning forest with restricted tree size is NP-hard, and develop an approximation algorithm for this problem. Viewing the tree size as a complexity parameter, we then select a forest using data splitting, and prove bounds on excess risk and structure selection consistency of the procedure. Experiments with simulated data and microarray data indicate that the methods are a practical alternative to Gaussian graphical models.

stat.ML↗

arXiv2010

Graph-Valued Regression

Undirected graphical models encode in a graph $G$ the dependency structure of a random vector $Y$. In many applications, it is of interest to model $Y$ given another random vector $X$ as input. We refer to the problem of estimating the graph $G(x)$ of $Y$ conditioned on $X=x$ as ``graph-valued regression.'' In this paper, we propose a semiparametric method for estimating $G(x)$ that builds a tree on the $X$ space just as in CART (classification and regression trees), but at each leaf of the tree estimates a graph. We call the method ``Graph-optimized CART,'' or Go-CART. We study the theoretical properties of Go-CART using dyadic partitioning trees, establishing oracle inequalities on risk minimization and tree partition consistency. We also demonstrate the application of Go-CART to a meteorological dataset, showing how graph-valued regression can provide a useful tool for analyzing complex data.

stat.ML↗

arXiv2010

Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

A challenging problem in estimating high-dimensional graphical models is to choose the regularization parameter in a data-dependent way. The standard techniques include $K$-fold cross-validation ($K$-CV), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Though these methods work well for low-dimensional problems, they are not suitable in high dimensional settings. In this paper, we present StARS: a new stability-based method for choosing the regularization parameter in high dimensional inference for undirected graphs. The method has a clear interpretation: we use the least amount of regularization that simultaneously makes a graph sparse and replicable under random sampling. This interpretation requires essentially no conditions. Under mild conditions, we show that StARS is partially sparsistent in terms of graph estimation: i.e. with high probability, all the true edges will be included in the selected model even when the graph size diverges with the sample size. Empirically, the performance of StARS is compared with the state-of-the-art model selection procedures, including $K$-CV, AIC, and BIC, on both synthetic data and a real microarray dataset. StARS outperforms all these competing procedures.

stat.ML↗

arXiv2009

The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We show how to use a semiparametric Gaussian copula--or "nonparanormal"--for high dimensional inference. Just as additive models extend linear models by replacing linear functions with a set of one-dimensional smooth functions, the nonparanormal extends the normal by transforming the variables by smooth functions. We derive a method for estimating the nonparanormal, study the method's theoretical properties, and show that it works well in many examples.

stat.ML↗

arXiv2008

Some Two-Step Procedures for Variable Selection in High-Dimensional Linear Regression

We study the problem of high-dimensional variable selection via some two-step procedures. First we show that given some good initial estimator which is $\ell_{\infty}$-consistent but not necessarily variable selection consistent, we can apply the nonnegative Garrote, adaptive Lasso or hard-thresholding procedure to obtain a final estimator that is both estimation and variable selection consistent. Unlike the Lasso, our results do not require the irrepresentable condition which could fail easily even for moderate $p_n$ (Zhao and Yu, 2007) and it also allows $p_n$ to grow almost as fast as $\exp(n)$ (for hard-thresholding there is no restriction on $p_n$). We also study the conditions under which the Ridge regression can be used as an initial estimator. We show that under a relaxed identifiable condition, the Ridge estimator is $\ell_{\infty}$-consistent. Such a condition is usually satisfied when $p_n\le n$ and does not require the partial orthogonality between relevant and irrelevant covariates which is needed for the univariate regression in (Huang et al., 2008). Our numerical studies show that when using the Lasso or Ridge as initial estimator, the two-step procedures have a higher sparsity recovery rate than the Lasso or adaptive Lasso with univariate regression used in (Huang et al., 2008).

math.ST↗

arXiv2008

Sparse Additive Models

We present a new class of methods for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. SpAM is closely related to the COSSO model of Lin and Zhang (2006), but decouples smoothing and sparsity, enabling the use of arbitrary nonparametric smoothers. An analysis of the theoretical properties of SpAM is given. We also study a greedy estimator that is a nonparametric version of forward stepwise regression. Empirical results on synthetic and real data are presented, showing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data.

math.ST↗

arXiv2008

On the $\ell_1-\ell_q$ Regularized Regression

In this paper we consider the problem of grouped variable selection in high-dimensional regression using $\ell_1-\ell_q$ regularization ($1\leq q \leq \infty$), which can be viewed as a natural generalization of the $\ell_1-\ell_2$ regularization (the group Lasso). The key condition is that the dimensionality $p_n$ can increase much faster than the sample size $n$, i.e. $p_n \gg n$ (in our case $p_n$ is the number of groups), but the number of relevant groups is small. The main conclusion is that many good properties from $\ell_1-$regularization (Lasso) naturally carry on to the $\ell_1-\ell_q$ cases ($1 \leq q \leq \infty$), even if the number of variables within each group also increases with the sample size. With fixed design, we show that the whole family of estimators are both estimation consistent and variable selection consistent under different conditions. We also show the persistency result with random design under a much weaker condition. These results provide a unified treatment for the whole family of estimators ranging from $q=1$ (Lasso) to $q=\infty$ (iCAP), with $q=2$ (group Lasso)as a special case. When there is no group structure available, all the analysis reduces to the current results of the Lasso estimator ($q=1$).

stat.ML↗

arXiv2006

Transverse Single Spin Asymmetry Measurement with J/Psi in Polarized p+p Collisions at RHIC

The PHENIX experiment has measured transverse single spin asymmetry of J/$Ψ$ in polarized p+p collisions at forward rapidity at $\sqrt{s}=200$ GeV. The data were collected from year 2006 run of RHIC with average beam polarization of 56%. At RHIC energy, heavy quark production is dominated by gluon gluon interaction. Therefore, the transverse single spin asymmetry in J/$Ψ$ production can provide a clean measurement of the gluon Sivers distribution function.

hep-ex↗