A Monte Carlo packing algorithm for poly-ellipsoids and its comparison with packing generation using Discrete Element Model

Granular material is showing very often in geotechnical engineering, petroleum engineering, material science and physics. The packings of the granular material play a very important role in their mechanical behaviors, such as stress-strain response, stability, permeability and so on. Although packing is such an important research topic that its generation has been attracted lots of attentions for a long time in theoretical, experimental, and numerical aspects, packing of granular material is still a difficult and active research topic, especially the generation of random packing of non-spherical particles. To this end, we will generate packings of same particles with same shapes, numbers, and same size distribution using geometry method and dynamic method, separately. Specifically, we will extend one of Monte Carlo models for spheres to ellipsoids and poly-ellipsoids

Statistical Modeling with the Virtual Source MOSFET Model

A statistical extension of the ultra-compact Virtual Source (VS) MOSFET model is developed here for the first time. The characterization uses a statistical extraction technique based on the backward propagation of variance (BPV) with variability parameters derived directly from the nominal VS model. The resulting statistical VS model is extensively validated using Monte Carlo simulations, and the statistical distributions of several figures of merit for logic and memory cells are compared with those of a BSIM model from a 40-nm CMOS industrial design kit. The comparisons show almost identical distributions with distinct run time advantages for the statistical VS model. Additional simulations show that the statistical VS model accurately captures non-Gaussian features that are important for low-power designs.Masdar Institute of Science and Technolog

A Two-level Prediction Model for Deep Reactive Ion Etch (DRIE)

We contribute a quantitative and systematic model to capture etch non-uniformity in deep reactive ion etch of microelectromechanical systems (MEMS) devices. Deep reactive ion etch is commonly used in MEMS fabrication where high-aspect ratio features are to be produced in silicon. It is typical for many supposedly identical devices, perhaps of diameter 10 mm, to be etched simultaneously into one silicon wafer of diameter 150 mm. Etch non-uniformity depends on uneven distributions of ion and neutral species at the wafer level, and on local consumption of those species at the device, or die, level. An ion–neutral synergism model is constructed from data obtained from etching several layouts of differing pattern opening densities. Such a model is used to predict wafer-level variation with an r.m.s. error below 3%. This model is combined with a die-level model, which we have reported previously, on a MEMS layout. The two-level model is shown to enable prediction of both within-die and wafer-scale etch rate variation for arbitrary wafer loadings.Singapore-MIT Alliance (SMA

Statistical library characterization using belief propagation across multiple technology nodes

In this paper, we propose a novel flow to enable computationally efficient statistical characterization of delay and slew in standard cell libraries. The distinguishing feature of the proposed method is the usage of a limited combination of output capacitance, input slew rate and supply voltage for the extraction of statistical timing metrics of an individual logic gate. The efficiency of the proposed flow stems from the introduction of a novel, ultra-compact, nonlinear, analytical timing model, having only four universal regression parameters. This novel model facilitates the use of maximum-a-posteriori belief propagation to learn the prior parameter distribution for the parameters of the target technology from past characterizations of library cells belonging to various other technologies, including older ones. The framework then utilises Bayesian inference to extract the new timing model parameters using an ultra-small set of additional timing measurements from the target technology. The proposed method is validated and benchmarked on several production-level cell libraries including a state-of-the-art 14-nm technology node and a variation-aware, compact transistor model. For the same accuracy as the conventional lookup-table approach, this new method achieves at least 15x reduction in simulation runs.Masdar Institute of Science and Technology (Massachusetts Institute of Technology Cooperative Agreement

Spatial variation decomposition via sparse regression

In this paper, we briefly discuss the recent development of a novel sparse regression technique that aims to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) uncorrelated random variation. Such variation decomposition is important to identify systematic variation patterns at wafer and/or chip level for process modeling, control and diagnosis. We demonstrate that the spatially correlated variation can be accurately represented by the linear combination of a small number of “templates”. Based upon this observation, an efficient algorithm is developed to accurately separate spatially correlated variation from uncorrelated random variation. Several examples based on silicon measurement data demonstrate that the aforementioned sparse regression technique can capture systematic variation patterns with high accuracy.Interconnect Focus Center (United States. Defense Advanced Research Projects Agency and Semiconductor Research Corporation)Focus Center Research Program. Focus Center for Circuit & System SolutionsNational Science Foundation (U.S.) (Contract CCF-0915912

An ultra-compact virtual source FET model for deeply-scaled devices: Parameter extraction and validation for standard cell libraries and digital circuits

In this paper, we present the first validation of the virtual source (VS) charge-based compact model for standard cell libraries and large-scale digital circuits. With only a modest number of physically meaningful parameters, the VS model accounts for the main short-channel effects in nanometer technologies. Using a novel DC and transient parameter extraction methodology, the model is verified with simulated data from a well-characterized, industrial 40-nm bulk silicon model. The VS model is used to fully characterize a standard cell library with timing comparisons showing less than 2.7% error with respect to the industrial design kit. Furthermore, a 1001-stage inverter chain and a 32-bit ripple-carry adder are employed as test cases in a vendor CAD environment to validate the use of the VS model for large-scale digital circuit applications. Parametric Vdd sweeps show that the VS model is also ready for usage in low-power design methodologies. Finally, runtime comparisons have shown that the use of the VS model results in a speedup of about 7.6×.Masdar Institute of Science and Technology (Massachusetts Institute of Technology Cooperative Agreement

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed “degradability”, is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision–recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development

Contemporary human H3N2 influenza a viruses require a low threshold of suitable glycan receptors for efficient infection

Recent human H3N2 influenza A viruses (IAV) have evolved to employ elongated glycans terminating in α2,6-linked sialic acid as their receptors. These glycans are displayed in low abundancies by (humanized) Madin-Darby Canine Kidney cells (MDCK and hCK) which are commonly employed to propagate IAV, resulting in low or no viral propagation. Here, we examined whether the overexpression of the glycosyltransferases B3GNT2 and B4GALT1, which are responsible for the elongation of poly-N-acetyllactosamines (LacNAc), would result in improved A/H3N2 propagation. Stable overexpression of B3GNT2 and B4GALT1 in MDCK and hCK cells was achieved by lentiviral integration and subsequent antibiotic selection and confirmed by qPCR and protein mass spectrometry experiments. Flow cytometry and glycan mass spectrometry experiments using the B3GNT2 and/or B4GALT1 knock-in cells demonstrated increased binding of viral hemagglutinins and the presence of a larger number of LacNAc repeating units, especially on hCK-B3GNT2 cells. An increase in the number of glycan receptors did, however, not result in a greater infection efficiency of recent human H3N2 viruses. Based on these results, we propose that H3N2 IAVs require a low number of suitable glycan receptors to infect cells and that an increase in the glycan receptor display above this threshold does not result in improved infection efficiency

Haemoglobin mass and running time trial performance after recombinant human erythropoietin administration in trained men

<p>Recombinant human erythropoietin (rHuEpo) increases haemoglobin mass (Hbmass) and maximal oxygen uptake (v˙ O2 max).</p> <p>Purpose: This study defined the time course of changes in Hbmass, v˙ O2 max as well as running time trial performance following 4 weeks of rHuEpo administration to determine whether the laboratory observations would translate into actual improvements in running performance in the field.</p> <p>Methods: 19 trained men received rHuEpo injections of 50 IUNkg21 body mass every two days for 4 weeks. Hbmass was determined weekly using the optimized carbon monoxide rebreathing method until 4 weeks after administration. v˙ O2 max and 3,000 m time trial performance were measured pre, post administration and at the end of the study.</p> <p>Results: Relative to baseline, running performance significantly improved by ,6% after administration (10:3061:07 min:sec vs. 11:0861:15 min:sec, p,0.001) and remained significantly enhanced by ,3% 4 weeks after administration (10:4661:13 min:sec, p,0.001), while v˙ O2 max was also significantly increased post administration (60.765.8 mLNmin21Nkg21 vs. 56.066.2 mLNmin21Nkg21, p,0.001) and remained significantly increased 4 weeks after rHuEpo (58.065.6 mLNmin21Nkg21, p = 0.021). Hbmass was significantly increased at the end of administration compared to baseline (15.261.5 gNkg21 vs. 12.761.2 gNkg21, p,0.001). The rate of decrease in Hbmass toward baseline values post rHuEpo was similar to that of the increase during administration (20.53 gNkg21Nwk21, 95% confidence interval (CI) (20.68, 20.38) vs. 0.54 gNkg21Nwk21, CI (0.46, 0.63)) but Hbmass was still significantly elevated 4 weeks after administration compared to baseline (13.761.1 gNkg21, p<0.001).</p> <p>Conclusion: Running performance was improved following 4 weeks of rHuEpo and remained elevated 4 weeks after administration compared to baseline. These field performance effects coincided with rHuEpo-induced elevated v˙ O2 max and Hbmass.</p&gt