arXiv subjects

Fan Chen

Publications and source records attributed to Fan Chen.

121 records · Page 7Linked to original sources

arXiv2021

Crystal plasticity model of residual stress in additive manufacturing

Selective laser melting is receiving increasing interest as an additive manufacturing technique. Residual stresses induced by the large temperature gradients and inhomogeneous cooling process can favour the generation of cracks. In this work, a crystal plasticity finite element model is developed to simulate the formation of residual stresses and to understand the correlation between plastic deformation, grain orientation and residual stresses in the additive manufacturing process. The temperature profile and grain structure from thermal-fluid flow and grain growth simulations are implemented into the crystal plasticity model. An element elimination and reactivation method is proposed to model the melting and solidification and to reinitialise state variables, such as the plastic deformation, in the reactivated elements. The accuracy of this method is judged against previous method based on the stiffness degradation of liquid regions by comparing the plastic deformation as a function of time induced by thermal stresses. The method is used to investigate residual stresses parallel and perpendicular to the laser scan direction, and the correlation with the maximum Schmid factor of the grains along those directions. The magnitude of the residual stress can be predicted as a function of the depth, grain orientation and position with respect to the molten pool.

cond-mat.mtrl-sci↗

arXiv2020

Targeted sampling from massive block model graphs with personalized PageRank

The paper provides statistical theory and intuition for personalized PageRank (called "PPR"): a popular technique that samples a small community from a massive network. We study a setting where the entire network is expensive to obtain thoroughly or to maintain, but we can start from a seed node of interest and "crawl" the network to find other nodes through their connections. By crawling the graph in a designed way, the PPR vector can be approximated without querying the entire massive graph, making it an alternative to snowball sampling. Using the degree-corrected stochastic block model, we study whether the PPR vector can select nodes that belong to the same block as the seed node. We provide a simple and interpretable form for the PPR vector, highlighting its biases towards high degree nodes outside the target block. We examine a simple adjustment based on node degrees and establish consistency results for PPR clustering that allows for directed graphs. These results are enabled by recent technical advances showing the elementwise convergence of eigenvectors. We illustrate the method with the massive Twitter friendship graph, which we crawl by using the Twitter application programming interface. We find that the adjusted and unadjusted PPR techniques are complementary approaches, where the adjustment makes the results particularly localized around the seed node, and that the bias adjustment greatly benefits from degree regularization.

cs.SI↗

arXiv2020

White Paper on 6G Drivers and the UN SDGs

The commercial launch of 6G communications systems and United Nations Sustainable Development Goals, UN SDGs, are both targeted for 2030. 6G communications is expected to boost global growth and productivity, create new business models and transform many aspects of society. The UN SDGs are a way of framing opportunities and challenges of a desirable future world and cover topics as broad as ending poverty, gender equality, climate change and smart cities. The relationship between these potentially mutually reinforcing forces is currently under-defined. Building on the vision for 6G, a review of megatrends, on-going activities on the relation of mobile communications to the UN SDGs and existing indicators, a novel linkage between 6G and the UN SDGs is proposed via indicators. The white paper has also launched the work of deriving new 6G related indicators to guide the research of 6G systems. The novel linkage is built on the envisaged three-fold role of 6G as a provider of services to help steer and support communities and countries towards reaching the UN SDGs, as an enabler of measuring tool for data collection to help reporting of indicators with hyperlocal granularity, and as a reinforcer of new ecosystems based on 6G technology enablers and 6G network of networks to be developed in line with the UN SDGs that incorporates future mobile communication technologies available in 2030. Related challenges are also identified. An action plan is presented along with prioritized focus areas within the mobile communication sector technology and industry evolution to best support the achievement of the UN SDGs.

eess.SP↗

arXiv2019

Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment

Emerging resistive random-access memory (ReRAM) has recently been intensively investigated to accelerate the processing of deep neural networks (DNNs). Due to the in-situ computation capability, analog ReRAM crossbars yield significant throughput improvement and energy reduction compared to traditional digital methods. However, the power hungry analog-to-digital converters (ADCs) prevent the practical deployment of ReRAM-based DNN accelerators on end devices with limited chip area and power budget. We observe that due to the limited bit-density of ReRAM cells, DNN weights are bit sliced and correspondingly stored on multiple ReRAM bitlines. The accumulated current on bitlines resulted by weights directly dictates the overhead of ADCs. As such, bitwise weight sparsity rather than the sparsity of the full weight, is desirable for efficient ReRAM deployment. In this work, we propose bit-slice L1, the first algorithm to induce bit-slice sparsity during the training of dynamic fixed-point DNNs. Experiment results show that our approach achieves 2x sparsity improvement compared to previous algorithms. The resulting sparsity allows the ADC resolution to be reduced to 1-bit of the most significant bit-slice and down to 3-bit for the others bits, which significantly speeds up processing and reduces power and area overhead.

cs.LG↗

arXiv2019

Hamilton-Jacobi Approach to Holographic Renormalization of Massive Gravity

Recently, a practical approach to holographic renormalization has been developed based on the Hamilton-Jacobi formulation. Using a simple Einstein-scalar theory, we clarify that this approach does not conflict with the Hamiltonian constraint as it seems. Then we apply it to the holographic renormalization of massive gravity. We assume that the shift vector is falling off fast enough asymptotically. We derive the counterterms up to the boundary dimension d=4. Interestingly, we find that the conformal anomaly can even occur in odd dimensions, which is different from the Einstein gravity. We check that the counterterms cancel the divergent part of the on-shell action at the background level. At the perturbation level, they are also applicable in several time-dependent cases.

hep-th↗

arXiv2019

Interaction-dependent anisotropy of fractional quantum Hall states

A fractional quantum Hall (FQH) system with broken rotational symmetry exploits its geometric degree of freedom to minimize its ground state energy. The mass anisotropy of bare particles interacting isotropically is partially inherited by the many-body FQH state, and the extent to which it does so depends on the type of interaction, filling fraction and ground state phase. Using numerical infinite density matrix renormalization group simulations, we investigate the transference of elliptical ($C_2$-symmetric) anisotropy from the band mass of the bare particles to the FQH states, for various power law interactions. We map out the response of FQH states to small anisotropy as a function of power law exponent, filling, and statistics (bosonic or fermionic) of the constituents. Interestingly, we find a non-analyticity in the linear response of the FQH state at a special filling-dependent value of the power law exponent, above which the interaction effectively becomes zero-range (point-like). We also investigate the the effect of $C_4$-symmetric band distortions, where we observe a strikingly different dependence on filling.

cond-mat.str-el↗

arXiv2019

Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data

We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resulting network achieves higher accuracy than previous results while requiring a smaller model size and less training time. In particular, the proposed model outperforms the state-of-the-art by 4.00% on classification accuracy. For the regression task, our model achieves 0.9919 on the coefficient of determination, higher than the previous work (0.96).

cs.LG↗

arXiv2017

Switching Mechanism and the Scalability of vertical-TFETs

In this work, vertical tunnel field-effect transistors (v-TFETs) based on vertically stacked heretojunctions from 2D transition metal dichalcogenide (TMD) materials are studied by atomistic quantum transport simulations. The switching mechanism of v-TFET is found to be different from previous predictions. As a consequence of this switching mechanism, the extension region, where the materials are not stacked over is found to be critical for turning off the v-TFET. This extension region makes the scaling of v-TFETs challenging. In addition, due to the presence of both positive and negative charges inside the channel, v-TFETs also exhibit negative capacitance. As a result, v-TFETs have good energy-delay products and are one of the promising candidates for low power applications.

cond-mat.mtrl-sci↗

arXiv2017

Hole Mobility Model for Si Double-Gate Junctionless Transistors

In this work, a physics based model is developed to calculate the hole mobility of ultra-thin-body double-gate junctionless transistors. Six-band $k\cdot p$ Schrödinger equation and Poisson equation are solved self-consistently. The obtained wave-functions and energies are stored in look-up tables. Hole mobility can be derived using the Kubo-Greenwood formula accounting for impurity, acoustic and optical phonon, and surface roughness scattering. Initial benchmark results are shown.

physics.app-ph↗

arXiv2017

Dramatic Impact of Dimensionality on the Electrostatics of PN Junctions

Low dimensional material systems provide a unique set of properties useful for solid-state devices. The building block of these devices is the PN junction. In this work, we present a dramatic difference in the electrostatics of PN junctions in lower dimensional systems, as against the well understood three dimensional systems. Reducing the dimensionality increases the depletion width significantly. We propose a novel method to derive analytic equations in 2D and 1D that considers the impact of neutral regions. The analytical results show an excellent match with both the experimental measurements and numerical simulations. The square root dependence of the depletion width on the ratio of dielectric constant and doping in 3D changes to a linear and exponential dependence for 2D and 1D respectively. This higher sensitivity of 1D PN junctions to its control parameters can be used towards new sensors.

cond-mat.mes-hall↗

arXiv2016

Impact of Dimensionality on PN Junctions

Low dimensional material systems provide a unique set of properties useful for solid-state devices. The building block of these devices is the PN junction. In this work, we present a dramatic difference in the electrostatics of PN junctions in lower dimensional systems, as against the well understood three dimensional systems. Reducing the dimensionality increases the fringing fields and depletion width significantly. We propose a novel method to derive analytic equations in 2D and 1D that considers the impact of neutral regions. The analytical results show an excellent match with both the experimental measurements and numerical simulations. The square root dependence of the depletion width on the ratio of dielectric constant and doping in 3D changes to a linear and exponential dependence for 2D and 1D respectively. This higher sensitivity of 1D PN junctions to its control parameters can be used towards new sensors.

cond-mat.mes-hall↗

arXiv2016

Transport in vertically stacked hetero-structures from 2D materials

In this work, the transport of tunnel field-effect transistor (TFET) based on vertically stacked hereto-structures from 2D transition metal dichalcogenide (TMD) materials is investigated by atomistic quantum transport simulations. WTe2-MoS2 combination was chosen due to the formation of a broken gap hetero-junction which is desirable for TFETs. There are two assumptions behind the MoS2-WTe2 hetero-junction tight binding (TB) model: 1) lattice registry. 2) The $S-Te$ parameters being the average of the $S-S$ and $Te-Te$ parameters of bilayer MoS2 and WTe2. The computed TB bandstructure of the hetero-junction agrees well with the bandstructure obtained from density functional theory (DFT) in the energy range of interest for transport. NEGF (Non-Equilibrium Green$'$s Function) equations within the tight binding description is then utilized for device transfer characteristic calculation. Results show 1) energy filtering is the switching mechanism; 2) the length of the extension region is critical for device to turn off; 3) MoS2-WTe2 interlayer TFET can achieve a large on-current of $1000 μA/μm$ with $V_{DD} = 0.3V$, which suggests interlayer TFET can solve the low ON current problem of TFETs and can be a promising candidate for low power applications.

cond-mat.mtrl-sci↗

arXiv2016

First principles study and empirical parametrization of twisted bilayer MoS2 based on band-unfolding

We explore the band structure and ballistic electron transport in twisted bilayer $\textrm{MoS}_2$ using Density Functional Theory (DFT). The sphagetti like bands are unfolded to generate band structures in the primitive unit cell of the original un-twisted $\textrm{MoS}_2$ bilayer and projected onto an individual layer. The corresponding twist angle dependent indirect bandedges are extracted from the unfolded band structures. Based on a comparison within the same primitive unit cell, an efficient two band effective mass model for indirect conduction and valence valleys is created and parameterized by fitting the unfolded band structures. With the two band effective mass model, transport properties - specifically, we calculate the ballistic transmission in arbitrarily twisted bilayer $\textrm{MoS}_2$.

cond-mat.mtrl-sci↗