arXiv subjects

Seyoung Kim

Publications and source records attributed to Seyoung Kim.

24 records · Page 2Linked to original sources

arXiv2010

Dielectric Thickness Dependence of Carrier Mobility in Graphene with HfO2 Top Dielectric

We investigate the carrier mobility in mono- and bi-layer graphene with a top HfO2 dielectric, as a function of the HfO2 film thickness and temperature. The results show that the carrier mobility decreases during the deposition of the first 2-4 nm of top dielectric and remains constant for thicker layers. The carrier mobility shows a relatively weak dependence on temperature indicating that phonon scattering does not play a dominant role in controlling the carrier mobility. The data strongly suggest that fixed charged impurities located in close proximity to the graphene are responsible for the mobility degradation.

cond-mat.mes-hall↗

arXiv2010

Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso

We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regression assume that all of the output variables are equally related to the inputs, although in many real-world problems, outputs are related in a complex manner. In this paper, we propose graph-guided fused lasso (GFlasso) for structured multi-task regression that exploits the graph structure over the output variables. We introduce a novel penalty function based on fusion penalty to encourage highly correlated outputs to share a common set of relevant inputs. In addition, we propose a simple yet efficient proximal-gradient method for optimizing GFlasso that can also be applied to any optimization problems with a convex smooth loss and the general class of fusion penalty defined on arbitrary graph structures. By exploiting the structure of the non-smooth ''fusion penalty'', our method achieves a faster convergence rate than the standard first-order method, sub-gradient method, and is significantly more scalable than the widely adopted second-order cone-programming and quadratic-programming formulations. In addition, we provide an analysis of the consistency property of the GFlasso model. Experimental results not only demonstrate the superiority of GFlasso over the standard lasso but also show the efficiency and scalability of our proximal-gradient method.

stat.ML↗

arXiv2009

Quantum Hall effect in dual-gated graphene bilayers with tunable layer density imbalance

We study the magnetotransport properties of dual-gated graphene bilayers, in which the total density and layer density imbalance are independently controlled. As the bilayer is imbalanced we observe the emergence of a quantum Hall state (QHS) at filling factor $ν=0$ evinced by a plateau in the Hall conductivity, consistent with the opening of a gap between the electron and hole bands. By varying the layer density imbalance at fixed total density, we observe a suppression of the QHS at filling factors $ν=8$ and $ν=12$ when the layer densities are balanced, an observation at variance with theoretical expectations in the absence of electron-electron interaction and disorder.

cond-mat.mes-hall↗

arXiv2009

Realization of a High Mobility Dual-gated Graphene Field Effect Transistor with Al2O3 Dielectric

We fabricate and characterize dual-gated graphene field-effect transistors (FETs) using Al2O3 as top-gate dielectric. We use a thin Al film as a nucleation layer to enable the atomic layer deposition of Al2O3. Our devices show mobility values of over 8,000 cm2/Vs at room temperature, a finding which indicates that the top-gate stack does not significantly increase the carrier scattering, and consequently degrade the device characteristics. We propose a device model to fit the experimental data using a single mobility value.

cond-mat.mes-hall↗

arXiv2009

Large-Area Synthesis of High-Quality and Uniform Graphene Films on Copper Foils

Graphene has been attracting great interest because of its distinctive band structure and physical properties. Today, graphene is limited to small sizes because it is produced mostly by exfoliating graphite. We grew large-area graphene films of the order of centimeters on copper substrates by chemical vapor deposition using methane. The films are predominantly single layer graphene with a small percentage (less than 5%) of the area having few layers, and are continuous across copper surface steps and grain boundaries. The low solubility of carbon in copper appears to help make this growth process self-limiting. We also developed graphene film transfer processes to arbitrary substrates, and dual-gated field-effect transistors fabricated on Si/SiO2 substrates showed electron mobilities as high as 4050 cm2V-1s-1 at room temperature.

cond-mat.mtrl-sci↗

arXiv2008

A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network

Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to address this issue in a principled way. Our approach explicitly represents the dependency structure among the quantitative traits as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits, so that the genetic markers that jointly influence subgroups of highly correlated traits can be detected with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical method, and borrow information across correlated phenotypes to discover the genetic markers that perturbe a subset of correlated triats jointly rather than a single trait. Using simulated datasets based on the HapMap consortium data and an asthma dataset, we compare the performance of our method with the single-marker analysis, and other sparse regression methods such as the ridge regression and the lasso that do not use any structural information in the traits. Our results show that there is a significant advantage in detecting the true causal SNPs when we incorporate the correlation pattern in traits using our proposed methods.

stat.ML↗