arXiv subjects

Yaqi Li

Publications and source records attributed to Yaqi Li.

41 records · Page 3Linked to original sources

arXiv2023

Ultra-high mobility semiconducting epitaxial graphene on silicon carbide

Graphene nanoelectronics potential was limited by the lack of an intrinsic bandgap[1] and attempts to tailor a bandgap either by quantum confinement or by chemical functionalization failed to produce a semiconductor with a large enough band gap and a sufficient mobility. It is well known that by evaporating silicon from commercial electronics grade silicon carbide crystals an epitaxial graphene layer forms on the surfaces [2]. The first epigraphene layer to form on the silicon terminated face, known as the buffer layer, is insulating. It is chemically bonded to the SiC and spectroscopic measurements [3] have identified semiconducting signatures on the microscopic domains. However, the bonding to the SiC is disordered and the mobilities are small. Here we demonstrate a quasi-equilibrium annealing method that produces macroscopic atomically flat terraces covered with a well ordered epigraphene buffer layer that has a 0.6 eV bandgap. Room temperature mobilities exceed 5000 cm2/Vs which is much larger than silicon and 20 times larger than the phonon scattering imposed limit of current 2D semiconductors. Critical for nanotechnology, its lattice is aligned with the SiC substrate, it is chemically, mechanically, and thermally robust, and it can be conventionally patterned and seamlessly connected to semimetallic epigraphene making semiconducting epigraphene ideally suited for nanoelectronics.

cond-mat.mes-hall↗

arXiv2023

Towards Layer-Selective Quantum Spin Hall Channels in Weak Topological Insulator Bi4Br2I2

Weak topological insulators, constructed by stacking quantum spin Hall insulators with weak interlayer coupling, offer promising quantum electronic applications through topologically nontrivial edge channels. However, the currently available weak topological insulators are stacks of the same quantum spin Hall layer with translational symmetry in the out-of-plane direction, leading to the absence of the channel degree of freedom for edge states. Here, we study a candidate weak topological insulator, Bi4Br2I2, which is alternately stacked by three different quantum spin Hall insulators, each with tunable topologically non-trivial edge states. Our angle-resolved photoemission spectroscopy and first-principles calculations show that an energy gap opens at the crossing points of different Dirac cones correlated with different layers due to the interlayer interaction. This is essential to achieve the tunability of topological edge states as controlled by varying the chemical potential. Our work offers a perspective for the construction of tunable quantized conductance devices for future spintronic applications.

cond-mat.mtrl-sci↗

arXiv2023

Human Machine Co-adaption Interface via Cooperation Markov Decision Process System

This paper aims to develop a new human-machine interface to improve rehabilitation performance from the perspective of both the user (patient) and the machine (robot) by introducing the co-adaption techniques via model-based reinforcement learning. Previous studies focus more on robot assistance, i.e., to improve the control strategy so as to fulfill the objective of Assist-As-Needed. In this study, we treat the full process of robot-assisted rehabilitation as a co-adaptive or mutual learning process and emphasize the adaptation of the user to the machine. To this end, we proposed a Co-adaptive MDPs (CaMDPs) model to quantify the learning rates based on cooperative multi-agent reinforcement learning (MARL) in the high abstraction layer of the systems. We proposed several approaches to cooperatively adjust the Policy Improvement among the two agents in the framework of Policy Iteration. Based on the proposed co-adaptive MDPs, the simulation study indicates the non-stationary problem can be mitigated using various proposed Policy Improvement approaches.

cs.AI↗

arXiv2021

Direct band gap and strong Rashba effect in van der Waals heterostructures of InSe and Sb single layers

Van der Waals heterostructures formed by stacking different types of 2D materials are attracting increasing attention due to new emergent physical properties such as interlayer excitons. Recently synthesized atomically thin indium selenide (InSe) and antimony (Sb) individually exhibit interesting electronic properties such as high electron mobility in the former and high hole mobility in the latter. In this work, we present a first-principles investigation on the stability and electronic properties of ultrathin bilayer heterostructures composed of InSe and Sb single layers. The calculated electronic band structures reveal a direct band gap semiconducting nature of the InSe/Sb heterostructures independent of stacking pattern. Taking spin-orbit coupling into account, we find a large Rashba spin splitting at the bottom of conduction band, which originates from the atomic spin-orbit coupling with the symmetry breaking in the heterostructure. The strength of the Rashba spin splitting can be tuned by applying in-plane biaxial strain or an out-of-plane external electric field. The presence of large Rashba spin splitting together with a suitable band gap in InSe/Sb bilayer heterostructures make them promising candidates for spin field-effect transistor and optoelectronic device applications.

cond-mat.mes-hall↗

arXiv2021

Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19

We propose the novel use of a generative adversarial network (GAN) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. To predict the spread of COVID-19 in an idealised town, we apply these methods to a compartmental model in epidemiology that is able to model space and time variations. To do this, the GAN is set within a reduced-order model (ROM), which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.

cs.LG↗