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Junjie Wang

Publications and source records attributed to Junjie Wang.

291 records · Page 17Linked to original sources

Gate-modulated conductance of few-layer WSe_2 field-effect transistors in the subgap regime: Schottky barrier transistor and subgap impurity states

Two key subjects stand out in the pursuit of semiconductor research: material quality and contact technology. The fledging field of atomically thin transition metal dichalcogenides (TMDCs) faces a number of challenges in both efforts. This work attempts to establish a connection between the two by examining the gate-dependent conductance of few-layer (1-5L) WSe2 field effect devices. Measurements and modeling of the subgap regime reveal Schottky barrier transistor behavior. We show that transmission through the contact barrier is dominated by thermionic field emission (TFE) at room temperature, despite the lack of intentional doping. The TFE process arises due to a large number of subgap impurity states, the presence of which also leads to high mobility edge carrier densities. The density of states of such impurity states is self-consistently determined to be approximately 1-2x10^13 /cm^2/eV in our devices. We demonstrate that substrate is unlikely to be a major source of the impurity states and suspect that lattice defects within the material itself are primarily responsible. Our experiments provide key information to advance the quality and understanding of TMDC materials and electrical devices.

cond-mat.mes-hall↗

Detecting Collusive Cliques in Futures Markets Based on Trading Behaviors from Real Data

In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other investors by applying similar trading behaviors for maximizing their personal benefits. In this paper, a method is proposed to detect the hidden collusive cliques involved in an instrument of future markets by first calculating the correlation coefficient between any two eligible unified aggregated time series of signed order volume, and then combining the connected components from multiple sparsified weighted graphs constructed by using the correlation matrices where each correlation coefficient is over a user-specified threshold. Experiments conducted on real order data from the Shanghai Futures Exchange show that the proposed method can effectively detect suspect collusive cliques. A tool based on the proposed method has been deployed in the exchange as a pilot application for futures market surveillance and risk management.

q-fin.TR↗

Characteristics of Real Futures Trading Networks

Futures trading is the core of futures business, and it is considered as one of the typical complex systems. To investigate the complexity of futures trading, we employ the analytical method of complex networks. First, we use real trading records from the Shanghai Futures Exchange to construct futures trading networks, in which nodes are trading participants, and two nodes have a common edge if the two corresponding investors appear simultaneously in at least one trading record as a purchaser and a seller respectively. Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks. Empirical results show that the futures trading networks exhibit features such as scale-free behavior with interesting odd-even-degree divergence in low-degree regions, small-world effect, hierarchical organization, power-law betweenness distribution, disassortative mixing, and shrinkage of both the average path length and the diameter as network size increases. To the best of our knowledge, this is the first work that uses real data to study futures trading networks, and we argue that the research results can shed light on the nature of real futures business.

q-fin.ST↗