arXiv subjects

Tianqi Wang

Publications and source records attributed to Tianqi Wang.

60 records · Page 4Linked to original sources

arXiv2019

Unraveling the Effect of Electron-Electron Interaction on Electronic Transport in High-Mobility Stannate Films

Contrary to the common belief that electron-electron interaction (EEI) should be negligible in s-orbital-based conductors, we demonstrated that the EEI effect could play a significant role on electronic transport leading to the misinterpretation of the Hall data. We show that the EEI effect is primarily responsible for an increase in the Hall coefficient in the La-doped SrSnO3 films below 50 K accompanied by an increase in the sheet resistance. The quantitative analysis of the magnetoresistance (MR) data yielded a large phase coherence length of electrons exceeding 450 nm at 1.8 K and revealed the electron-electron interaction being accountable for breaking of electron phase coherency in La-doped SrSnO3 films. These results while providing critical insights into the fundamental transport behavior in doped stannates also indicate the potential applications of stannates in quantum coherent electronic devices owing to their large phase coherence length.

cond-mat.mtrl-sci↗

arXiv2019

Fully Integrated On-FPGA Molecular Dynamics Simulations

The implementation of Molecular Dynamics (MD) on FPGAs has received substantial attention. Previous work, however, has consisted of either proof-of-concept implementations of components, usually the range-limited force; full systems, but with much of the work shared by the host CPU; or prototype demonstrations, e.g., using OpenCL, that neither implement a whole system nor have competitive performance. In this paper, we present what we believe to be the first full-scale FPGA-based simulation engine, and show that its performance is competitive with a GPU (running Amber in an industrial production environment). The system features on-chip particle data storage and management, short- and long-range force evaluation, as well as bonded forces, motion update, and particle migration. Other contributions of this work include exploring numerous architectural trade-offs and analysis on various mappings schemes among particles/cells and the various on-chip compute units. The potential impact is that this system promises to be the basis for long timescale Molecular Dynamics with a commodity cluster.

cs.DC↗

arXiv2019

Separating Electrons and Donors in BaSnO3 via Band Engineering

Through a combination of thin film growth, hard X-ray photoelectron spectroscopy (HAXPES), scanning transmission electron microscopy/electron energy loss spectroscopy (STEM/EELS), magneto-transport measurements, and transport modeling, we report on the demonstration of modulation-doping of BaSnO3 (BSO) using a wider bandgap La-doped SrSnO3 (LSSO) layer. Hard X-ray photoelectron spectroscopy (HAXPES) revealed a valence band offset of 0.71 +/- 0.02 eV between LSSO and BSO resulting in a favorable conduction band offset for remote doping of BSO using LSSO. Nonlinear Hall effect of LSSO/BSO heterostructure confirmed two-channel conduction owing to electron transfer from LSSO to BSO and remained in good agreement with the results of self-consistent solution to one-dimensional Poisson and Schrödinger equations. Angle-dependent HAXPES measurements revealed a spatial distribution of electrons over 2-3 unit cells in BSO. These results bring perovskite oxides a step closer to room-temperature oxide electronics by establishing modulation-doping approaches in non-SrTiO3-based oxide heterostructure.

cond-mat.mtrl-sci↗

arXiv2018

Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels

Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. However, the huge number of antennas poses a challenge to conventional CSI feedback reduction methods and leads to excessive feedback overhead. In this article, we develop a real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), by extending a novel deep learning (DL)-based CSI sensing and recovery network. CsiNet-LSTM considerably enhances recovery quality and improves trade-off between compression ratio (CR) and complexity by directly learning spatial structures combined with time correlation from training samples of time-varying massive MIMO channels. Simulation results demonstrate that CsiNet- LSTM outperforms existing compressive sensing-based and DLbased methods and is remarkably robust to CR reduction.

cs.IT↗

arXiv2017

Deep Learning for Wireless Physical Layer: Opportunities and Challenges

Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning (DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system (for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.

cs.IT↗

arXiv2016

Heavy Ion Induced SEU Sensitivity Evaluation of 3D Integrated SRAMs

Heavy ions induced single event upset (SEU) sensitivity of three-dimensional integrated SRAMs are evaluated by using Monte Carlo sumulation methods based on Geant4. The cross sections of SEUs and Multi Cell Upsets (MCUs) for 3D SRAM are simulated by using heavy ions with different energies and LETs. The results show that the sensitivity of different die of 3D SRAM has obvious discrepancies at low LET. Average percentage of MCUs of 3D SRAMs rises from 17.2% to 32.95% when LET increases from 42.19 MeV cm2/mg to 58.57MeV cm2/mg. As for a certain LET, the percentage of MCUs shows a notable distinction between face-to-face structure and back-to-face structure. For back-to-face structure, the percentage of MCUs increases with the deeper die. However, the face-to-face die presents the relatively low percentage of MCUs. The comparison of SEU cross sections for planar SRAMs and experiment data are conducted to indicate the effectiveness of our simulation method. Finally, we compare the upset cross sections of planar process and 3D integrated SRAMs. Results demonstrate that the sensitivity of 3D SRAMs is not more than that of planar SRAMs and the 3D structure can be become a great potential application for aerospace and military domain.

physics.ins-det↗