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For many years, the computer industry has relied on steady progress in the exponential rate of scaling MOSFETs in integrated circuits. The usual expectation, based on Moore's law, is that the number of transistors able to be packed on a chip doubles roughly every 18 months. Sustaining this pace requires aggressive research into the numerous bottlenecks that threaten to slow it down. Much research has gone into the photolithography needed to produce such dense circuits, device structures that would allow smaller channel lengths, and a plethora of other materials and device advances that help sustain the present rate of scaling. In the past decade, however, another issue has emerged that threatens to impose an absolute limit on how many transistors can be packed onto a die. This is the issue of heat dissipation.

P2PLoc envisions wearable Internet of Things devices that compute the relative positions of each user, resulting in a topology or configuration of mobile users that can be tracked in real time for group-motion applications.

In this paper, we describe the development of moving mesh adaptation framework and its application to charge transport simulation of semiconductor devices, with emphasis on its relevance to power semiconductor devices. Mesh adaptivity in the context of semiconductor device simulation is an important problem and can help deal with the convergence and numerical stability issues, as well as automate the meshing process. We demonstrate the efficacy of our proposed meshing scheme through the simulation of a GaN-based power diode, as well as a Si diode with a non-rectangular doping profile, by externally coupling our framework to Sentaurus Device TCAD. We perform error analysis and compare our results with simulations based on high-resolution uniform structured meshes as well as manually refined axis-aligned meshes. In addition to the benefits in terms of accuracy, automation, and generality, our method can be regarded as a stepping stone toward computationally scalable and adaptive semiconductor device simulations.

In this paper, we develop a convolutional neural network model to predict the mechanical properties of a two-dimensional checkerboard composite quantitatively. The checkerboard composite possesses two phases: one phase is soft and ductile while the other is stiff and brittle. The ground-truth data used in the training process are obtained from finite element analyses under the assumption of plane stress. Monte Carlo simulations and central limit theorem are used to find the size of the dataset needed. Once the training process is completed, the developed model is validated using data unseen during training. The developed neural network model captures the stiffness, strength, and toughness of checkerboard composites with high accuracy. Also, we integrate the developed model with a genetic algorithm (GA) optimizer to identify the optimal microstructural designs. The genetic algorithm optimizer adopted here has several operators: selection, crossover, mutation, and elitism. The optimizer converges to configurations with highly enhanced properties. For the case of the modulus and starting from randomly-initialized generation, the GA optimizer converges to the global maximum which involves no soft elements. Also, the GA optimizers, when used to maximize strength and toughness, tend towards having soft elements in the region next to the crack tip.

There have been many efforts to identify the relation between the geometric structure of carbon-nanomaterials and the intensity profile of Raman spectra. As a result, researchers have reported intensity profiles changing with the geometric properties of carbon-nanomaterials, i.e. the length of carbon nanotubes and the thickness of graphene. Based on these measured data, we constructed an autonomous framework that can deduce the geometric property from a Raman spectra pattern using a machine learning algorithm. In this work, we focus on the Raman peak shift recognition using principal component analysis (PCA) to identify the number of graphene layers and this framework can accelerate processes in both measurement and geometric property analysis.

New designs for vertical 2D-materials-based TFETs are proposed in this paper adopting asymmetric layer numbers for the top and bottom layer with undoped source/drain using Black Phosphorus as an example. The results show that abrupt turn-on and I on /I off > 105 can be sustained when the channel length is down to sub-5 nm. The results are benchmarked against other TFETs based on promising 2D materials homo-/hetero-structures, meanwhile, the limitations, as well as guidelines, are presented.

A new vertical tunnel FET design based on black phosphorus is presented in this paper adopting asymmetric layer numbers for top and bottom layer with undoped drain. The results show that the SS and I on /I off can be maintained below 10 mV/dec and beyond 105, respectively, when channel length is down to 3 nm.

SummaryClustering is one of the most common techniques used in data analysis to discover hidden structures by grouping together data points that are similar in some measure into clusters. Although there are many programs available for performing clustering, a single web resource that provides both state-of-the-art clustering methods and interactive visualizations is lacking. ClusterEnG (acronym for Clustering Engine for Genomics) provides an interface for clustering big data and interactive visualizations including 3D views, cluster selection and zoom features. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides clustering tutorials that demonstrate potential pitfalls of each algorithm. The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner.AvailabilityClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.Contactsongi@illinois.edu

In this paper, we propose a laterally graded-channel pseudo-junctionless (GPJL) MOSFET for analog/RF applications. We examine the dynamical performance of GPJL MOSFET and compare it with the common junctionless (JL) MOSFET architecture using a 2-D full-band electron Monte Carlo simulator (MC) with quantum correction. Our results indicate that the GPJL MOSFET outperforms the conventional JL MOSFET, yielding higher values of drain current (I ds), transconductance (g m), and cutoff frequency (f t). Further, the emerging electric field and velocity distributions, as a consequence of the channel engineering introduced by the GPJL MOSFET, result in lower output conductance (g ds) and higher early voltage (V ea). The preeminence of the GPJL transistor over the JL transistor is further illustrated by showing improvements on the intrinsic voltage gain (A vo) in the subthreshold regime, to as high as 61 %. These results indicate that our proposed GPJL MOSFET yields improvement in the analog/RF performance metrics as compared to JL MOSFETs.

Ion channels, as natures' solution to regulating biological environments, are particularly interesting to device engineers seeking to understand how natural molecular systems realize device-like functions, such as stochastic sensing of organic analytes. What's more, attaching molecular adaptors in desired orientations inside genetically engineered ion channels, enhances the system functionality as a biosensor. In general, a hierarchy of simulation methodologies is needed to study different aspects of a biological system like ion channels. Biology Monte Carlo (BioMOCA), a three-dimensional coarse-grained particle ion channel simulator, offers a powerful and general approach to study ion channel permeation. BioMOCA is based on the Boltzmann Transport Monte Carlo (BTMC) and Particle-Particle-Particle-Mesh (P(3)M) methodologies developed at the University of Illinois at Urbana-Champaign. In this paper, we have employed BioMOCA to study two engineered mutations of α-HL, namely (M113F)(6)(M113C-D8RL2)(1)-β-CD and (M113N)(6)(T117C-D8RL3)(1)-β-CD. The channel conductance calculated by BioMOCA is slightly higher than experimental values. Permanent charge distributions and the geometrical shape of the channels gives rise to selectivity towards anions and also an asymmetry in I-V curves, promoting a rectification largely for cations.

Background Clustering is one of the most common techniques in data analysis and seeks to group together data points that are similar in some measure. Although there are many computer programs available for performing clustering, a single web resource that provides several state-of-the-art clustering methods, interactive visualizations and evaluation of clustering results is lacking. Methods ClusterEnG (acronym for Clustering Engine for Genomics) provides a web interface for clustering data and interactive visualizations including 3D views, data selection and zoom features. Eighteen clustering validation measures are also presented to aid the user in selecting a suitable algorithm for their dataset. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides tutorials that demonstrate potential pitfalls of each algorithm. Conclusions The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner. The validation measures facilitate the process of choosing a suitable clustering algorithm among the available options. ClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.

We model and compare the thermal conductivity of rough semiconductor nanowires (NWs) of Si, Ge, and GaAs for thermoelectric devices. On the basis of full phonon dispersion relations, the effect of NW surface roughness on thermal conductivity is derived from perturbation theory and appears as an efficient way to scatter phonons in Si, Ge, and GaAs NWs with diameter D < 200 nm. For small diameters and large root-mean-square roughness Delta, thermal conductivity is limited by surface asperities and varies quadratically as (D/Delta)(2). At room temperature, our model previously agreed with experimental observations of thermal conductivity down to 2 W m(-1) K(-1) in rough 56 nm Si NWs with Delta = 3 nm. In comparison to Si, we predict here remarkably low thermal conductivity in Ge and GaAs NWs of 0.1 and 0.4 W m(-1) K(-1), respectively, at similar roughness and diameter.

We investigated the initial coupling of agonist binding to channel gating of the nicotinic acetylcholine receptor using targeted molecular-dynamics (TMD) simulation. After TMD simulation to accelerate closure of the C-loops at the agonist binding sites, the region of the pore that passes through the cell membrane expands. To determine whether the structural changes in the pore result in ion conduction, we used a coarse-grained ion conduction simulator, Biology Boltzmann transport Monte Carlo, and applied it to two structural frames taken before and after TMD simulation. The structural model before TMD simulation represents the channel in the proposed “resting” state, whereas the model after TMD simulation represents the channel in the proposed “active” state. Under external voltage biases, the channel in the “active” state was permeable to cations. Our simulated ion conductance approaches that obtained experimentally and recapitulates several functional properties characteristic of the nicotinic acetylcholine receptor. Thus, closure of the C-loop triggers a structural change in the channel sufficient to account for the open channel current. This approach of applying Biology Boltzmann transport Monte Carlo simulation can be used to further investigate the binding to gating transduction mechanism and the structural bases for ion selection and translocation.

This paper analyzes the design tradeoffs between Si and SiGe MITATT diodes using a Monte Carlo method. The narrow bandgap material Si 0.7 Ge 0.3 improves the charge confinement in the avalanche generation region and therefore increases the RF power generation efficiency. A SiGe MITATT diode operating at 200 GHz can produce 42 mW of RF power which is 63% higher than that from a Si MITATT diode. Beyond 300 GHz, the superior thermal properties of Si allow the Si MITATT diode to be driven at higher DC power level. Therefore, 2.3 mW can be generated from the Si MITATT diode at 400 GHz but only 1.5 mW from the SiGe MITATT diode.