Voltage and Temperature Scalable Gate Delay and Slew Models Including Intra-Gate Variations

We investigate the feasibility of developing a comprehensive gate delay and slew models which incorporates output load, input edge slew, supply voltage, temperature, global process variations and local process variations all in the same model. We find that the standard polynomial models cannot handle such a large heterogeneous set of input variables. We instead use neural networks, which are well known for their ability to approximate any arbitrary continuous function. Our initial experiments with a small subset of standard cell gates of an industrial 65 nm library show promising results with error in mean less than 1%, error in standard deviation less than 3% and maximum error less than 11% as compared to SPICE for models covering 0.9- 1.1 V of supply, -40degC to 125degC of temperature, load, slew and global and local process parameters. Enhancing the conventional libraries to be voltage and temperature scalable with similar accuracy requires on an average 4x more SPICE characterization runs.

Voltage and Temperature Scalable Gate Delay and Slew Models Including Intra-Gate Variations

We investigate the feasibility of developing a comprehensive gate delay and slew models which incorporates output load, input edge slew, supply voltage, temperature, global process variations and local process variations all in the same model. We find that the standard polynomial models cannot handle such a large heterogeneous set of input variables. We instead use neural networks, which are well known for their ability to approximate any arbitrary continuous function. Our initial experiments with a small subset of standard cell gates of an industrial 65 nm library show promising results with error in mean less than 1%, error in standard deviation less than 3% and maximum error less than 11% as compared to SPICE for models covering 0.9- 1.1 V of supply, -40degC to 125degC of temperature, load, slew and global and local process parameters. Enhancing the conventional libraries to be voltage and temperature scalable with similar accuracy requires on an average 4x more SPICE characterization runs.

Size and temperature dependent stability and phase transformation in single-crystal zirconium nanowire

A novel size dependent FCC (face-centered-cubic) -> HCP (hexagonally-closed-pack) phase transformation and stability of an initial FCC zirconium nanowire are studied. FCC zirconium nanowires with cross-sectional dimensions < 20 are found unstable in nature, and they undergo a FCC -> HCP phase transformation, which is driven by tensile surface stress induced high internal compressive stresses. FCC nanowire with cross-sectional dimensions > 20 , in which surface stresses are not enough to drive the phase transformation, show meta-stability. In such a case, an external kinetic energy in the form of thermal heating is required to overcome the energy barrier and achieve FCC -> HCP phase transformation. The FCC-HCP transition pathway is also studied using Nudged Elastic Band (NEB) method, to further confirm the size dependent stability/metastability of Zr nanowires. We also show size dependent critical temperature, which is required for complete phase transformation of a metastable-FCC nanowire.

Voltage and Temperature-Aware SSTA Using Neural Network Delay Model

With the emergence of voltage scaling as one of the most powerful power reduction techniques, it has been important to support voltage scalable statistical static timing analysis (SSTA) in deep submicrometer process nodes. In this paper, we propose a single delay model of logic gate using neural network which comprehensively captures process, voltage, and temperature variation along with input slew and output load. The number of simulation programs with integrated circuit emphasis (SPICE) required to create this model over a large voltage and temperature range is found to be modest and 4x less than that required for a conventional table-based approach with comparable accuracy. We show how the model can be used to derive sensitivities required for linear SSTA for an arbitrary voltage and temperature. Our experimentation on ISCAS 85 benchmarks across a voltage range of 0.9-1.1V shows that the average error in mean delay is less than 1.08% and average error in standard deviation is less than 2.85%. The errors in predicting the 99% and 1% probability point are 1.31% and 1%, respectively, with respect to SPICE. The two potential applications of voltage-aware SSTA have been presented, i.e., one for improving the accuracy of timing analysis by considering instance-specific voltage drops in power grids and the other for determining optimum supply voltage for target yield for dynamic voltage scaling applications.

Structure and temperature dependence of conductivity of Ag2.5Se‐Se composite

Rapid solidification of Ag‐53 at. % Se alloy resulted in the formation of a composite mixture of Ag2.5Se and Se. The microstructure consists of spherical Se grains of 2–20 μm size, randomly distributed in a matrix of Ag2.5 Se. The Se grains were found to be layered hexagonal while the Ag2.5 Se had an orthorhombic crystal structure. The unit cell size of this phase, however, was twice that reported for the equilibrium orthorhombic Ag2 Se compound. The conductivity σ variation with temperature in the range 80–320 K was found to be similar to that observed in degenerate semiconductors. The σ decreased from 295 Ω−1 cm−1 at room temperature to a saturation value of 70 Ω−1 cm−1 for temperatures <80 K. The results are discussed in terms of percolation conduction in the Ag2.5 Se phase.

In-situ measurements of concentration and temperature during transient solidification of aqueous solution of ammonium chloride using laser interferometry

The variation in temperature and concentration plays a crucial role in predicting the final microstructure during solidification of a binary alloy. Most of the experimental techniques used to measure concentration and temperature are intrusive in nature and affect the flow field. In this paper, the main focus is laid on in-situ, non-intrusive, transient measurement of concentration and temperature during the solidification of a binary mixture of aqueous ammonium chloride solution (a metal-analog system) in a top cooled cavity using laser based Mach-Zehnder Interferometric technique. It was found from the interferogram, that the angular deviation of fringe pattern and the total number of fringes exhibit significant sensitivity to refractive index and hence are functions of the local temperature and concentration of the NH4Cl solution inside the cavity. Using the fringe characteristics, calibration curves were established for the range of temperature and concentration levels expected during the solidification process. In the actual solidification experiment, two hypoeutectic solutions (5% and 15% NH4Cl) were chosen. The calibration curves were used to determine the temperature and concentration of the solution inside the cavity during solidification of 5% and 15% NH4Cl solution at different instants of time. The measurement was carried out at a fixed point in the cavity, and the concentration variation with time was recorded as the solid-liquid interface approached the measurement point. The measurement exhibited distinct zones of concentration distribution caused by solute rejection and Rayleigh Benard convection. Further studies involving flow visualization with laser scattering confirmed the Rayleigh Benard convection. Computational modeling was also performed, which corroborated the experimental findings. (C) 2011 Elsevier Ltd. All rights reserved.

Effect of strain rate and temperature on the plastic deformation behaviour of a bulk metallic glass composite

The composites consisting of amorphous matrix reinforced with crystalline dendrites offer extraordinary combinations of strength, stiffness, and toughness and can be processed in bulk. Hence, they have been receiving intense research interest, with a primary focus to study their mechanical properties. In this paper, the temperature and strain rate effects on the uniaxial compression response of a tailored bulk metallic glass (BMG) composite has been investigated. Experimental results show that at temperatures ranging between ambient to 500 K and at all strain rates; the onset of plastic deformation in the composite is controlled by that in the dendrites. As the temperature is increased to the glass transition temperature of the matrix and beyond, flow in the amorphous matrix occurs readily and hence it dictates the composite's response. The role of the constituent phases in controlling the deformation mechanism of the composite has been verified by assessing the strain rate sensitivity and the activation volume for deformation. The composite is rate sensitive at room temperature with values of strain rate sensitivity and activation volume being similar to that of the dendrites. At test temperatures near to the glass transition temperature, the composite however becomes rate-insensitive corresponding to that of the matrix phase. At low strain rates, serrated flow akin to that of dynamic strain ageing in crystalline alloys was observed and the serration magnitude decreases with increasing temperature. Initiation of the shear bands at the dendrite/matrix interface and propagation of them through the matrix ligaments until their arrest at another interface is the responsible mechanism for this. (C) 2011 Elsevier B.V. All rights reserved.

Strain induced phase transition in CdSe nanowires: Effect of size and temperature

Using all-atom molecular dynamics simulation, we have studied the effect of size and temperature on the strain induced phase transition of wurtzite CdSe nanowires. The wurtzite structure transforms into a five-fold coordinated structure under uniaxial strain along the c axis. Our results show that lower temperature and smaller size of the nanowires stabilize the five-fold coordinated phase which is not a stable structure in bulk CdSe. High reversibility of this transformation with a very small heat loss will make these nanowires suitable for building efficient nanodevices. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4734990]

Phonon anomalies and structural transition in spin ice Dy2Ti2O7: a simultaneous pressure-dependent and temperature-dependent Raman study

We revisit the assignment of Raman phonons of rare-earth titanates by performing Raman measurements on single crystals of O18 isotope-rich spin ice Dy2Ti2O718 and nonmagnetic Lu2Ti2O718 pyrochlores and compare the results with their O16 counterparts. We show that the low-wavenumber Raman modes below 250 cm-1 are not due to oxygen vibrations. A mode near 200 cm-1, commonly assigned as F2g phonon, which shows highly anomalous temperature dependence, is now assigned to a disorder-induced Raman active mode involving Ti4+ vibrations. Moreover, we address here the origin of the new Raman mode, observed below TC similar to 110 K in Dy2Ti2O7, through a simultaneous pressure-dependent and temperature-dependent Raman study. Our study confirms the new mode to be a phonon mode. We find that dTC/dP = + 5.9 K/GPa. Temperature dependence of other phonons has also been studied at various pressures up to similar to 8 GPa. We find that pressure suppresses the anomalous temperature dependence. The role of the inherent vacant sites present in the pyrochlore structure in the anomalous temperature dependence is also discussed. Copyright (c) 2012 John Wiley & Sons, Ltd.

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

Trends and Variability of AVHRR-Derived NPP in India

In this paper, we estimate the trends and variability in Advanced Very High Resolution Radiometer (AVHRR)-derived terrestrial net primary productivity (NPP) over India for the period 1982-2006. We find an increasing trend of 3.9% per decade (r = 0.78, R-2 = 0.61) during the analysis period. A multivariate linear regression of NPP with temperature, precipitation, atmospheric CO2 concentration, soil water and surface solar radiation (r = 0.80, R-2 = 0.65) indicates that the increasing trend is partly driven by increasing atmospheric CO2 concentration and the consequent CO2 fertilization of the ecosystems. However, human interventions may have also played a key role in the NPP increase: non-forest NPP growth is largely driven by increases in irrigated area and fertilizer use, while forest NPP is influenced by plantation and forest conservation programs. A similar multivariate regression of interannual NPP anomalies with temperature, precipitation, soil water, solar radiation and CO2 anomalies suggests that the interannual variability in NPP is primarily driven by precipitation and temperature variability. Mean seasonal NPP is largest during post-monsoon and lowest during the pre-monsoon period, thereby indicating the importance of soil moisture for vegetation productivity.

Cl-35 NQR frequency and spin lattice relaxation time in 3,4-dichlorophenol as a function of pressure and temperature

The pressure dependences of Cl-35 nuclear quadrupole resonance (NQR) frequency, temperature and pressure variation of spin lattice relaxation time (T-1) were investigated in 3,4-dichlorophenol. T-1 was measured in the temperature range 77-300 K. Furthermore, the NQR frequency and T-1 for these compounds were measured as a function of pressure up to 5 kbar at 300 K. The temperature dependence of the average torsional lifetimes of the molecules and the transition probabilities W-1 and W-2 for the Delta m = +/- 1 and Delta m = +/- 2 transitions were also obtained. A nonlinear variation of NQR frequency with pressure has been observed and the pressure coefficients were observed to be positive. A thermodynamic analysis of the data was carried out to determine the constant volume temperature coefficients of the NQR frequency. An attempt is made to compare the torsional frequencies evaluated from NQR data with those obtained by IR spectra. On selecting the appropriate mode from IR spectra, a good agreement with torsional frequency obtained from NQR data is observed. The previously mentioned approach is a good illustration of the supplementary nature of the data from IR studies, in relation to NQR studies of compounds in solid state.

Analysis of size quantization and temperature effects on the threshold voltage of thin silicon film double-gate metal-oxide-semiconductor field-effect transistor (MOSFET)

In this paper, we analyze the combined effects of size quantization and device temperature variations (T = 50K to 400 K) on the intrinsic carrier concentration (n(i)), electron concentration (n) and thereby on the threshold voltage (V-th) for thin silicon film (t(si) = 1 nm to 10 nm) based fully-depleted Double-Gate Silicon-on-Insulator MOSFETs. The threshold voltage (V-th) is defined as the gate voltage (V-g) at which the potential at the center of the channel (Phi(c)) begins to saturate (Phi(c) = Phi(c(sat))). It is shown that in the strong quantum confinement regime (t(si) <= 3nm), the effects of size quantization far over-ride the effects of temperature variations on the total change in band-gap (Delta E-g(eff)), intrinsic carrier concentration (n(i)), electron concentration (n), Phi(c(sat)) and the threshold voltage (V-th). On the other hand, for t(si) >= 4 nm, it is shown that size quantization effects recede with increasing t(si), while the effects of temperature variations become increasingly significant. Through detailed analysis, a physical model for the threshold voltage is presented both for the undoped and doped cases valid over a wide-range of device temperatures, silicon film thicknesses and substrate doping densities. Both in the undoped and doped cases, it is shown that the threshold voltage strongly depends on the channel charge density and that it is independent of incomplete ionization effects, at lower device temperatures. The results are compared with the published work available in literature, and it is shown that the present approach incorporates quantization and temperature effects over the entire temperature range. We also present an analytical model for V-th as a function of device temperature (T). (C) 2013 AIP Publishing LLC.