ANN Model For SiGe HBTs Constructed From Time-Domain Large-Signal Measurements

We construct a large-signal artificial neural network (ANN) model for SiGe HBTs, directly from time-domain large-signal measurements. It is known that HBTs are very sensitive to self-heating and therefore we explicitly study the effect on the model accuracy of the incorporation of the self-heating effect in the behavioural model description. Finally, we show that this type of models can be accurate at extreme operating conditions, where classical compact models start to fail

Correlation between the reliability of HEMT devices and that of a combined oscillator-amplifier

We evaluate an oscillator-amplifier MMIC submitted to high-temperature operating life time tests. To relate adequately these results with individual components’ results, it is important to realise that failure mechanisms in non-linear MMICs are governed by the maximally instantaneous voltages/currents and hence that comparisons should be conducted at equal instantaneous conditions

Extraction of small-signal model parameters of Si/SiGe heterojunction bipolar transistor using least squares support vector machines

A novel straightforward methodology for extracting bias-dependent small-signal equivalent circuit model parameters (SSECMPs) of silicon/silicon–germanium heterojunction bipolar transistors is presented. The inverse mapping between SSECMPs and scattering (S) parameters is established and fitted using simulated data of the SSECM. Since the problem has large input space, S-parameters at many frequency points, the least squares support vector machines concept is used as regression technique. Physical SSECMPs values are obtained using the proposed methodology. Moreover, an excellent agreement is noted between the S-parameters measurements and their simulated counterpart using the extracted SSECMPs in the frequency range from 40 MHz to 40 GHz at different bias conditions

Analysis of Coplanar On-Chip Interconnects on Lossy Semiconducting Substrates

In this paper, a method for analysis and modeling of coplanar transmission interconnect lines that are placed on top of silicon-silicon oxide substrates is presented. The potential function is expressed by series expansions in terms of solutions of the Laplace equation for each homogeneous region of layered structure. The expansion coefficients of different series are related to each other and to potentials applied to the conductors via boundary conditions. In the plane of conductors, boundary conditions are satisfied at Nd discrete points with Nd being equal to the number of terms in the series expansions. The resulting system of inhomogeneous linear equations is solved by matrix inversion. No iterations are required. A discussion of the calculated line admittance parameters as functions of width of conductors, thickness of the layers, and frequency is given. The interconnect capacitance and conductance per unit length results are given and compared with those obtained using full wave solutions, and good agreement have been obtained in all the cases treated

Diagnostic accuracy of pulmonary host inflammatory mediators in the exclusion of ventilator-acquired pneumonia.

BACKGROUND: Excessive use of empirical antibiotics is common in critically ill patients. Rapid biomarker-based exclusion of infection may improve antibiotic stewardship in ventilator-acquired pneumonia (VAP). However, successful validation of the usefulness of potential markers in this setting is exceptionally rare. OBJECTIVES: We sought to validate the capacity for specific host inflammatory mediators to exclude pneumonia in patients with suspected VAP. METHODS: A prospective, multicentre, validation study of patients with suspected VAP was conducted in 12 intensive care units. VAP was confirmed following bronchoscopy by culture of a potential pathogen in bronchoalveolar lavage fluid (BALF) at >10(4) colony forming units per millilitre (cfu/mL). Interleukin-1 beta (IL-1β), IL-8, matrix metalloproteinase-8 (MMP-8), MMP-9 and human neutrophil elastase (HNE) were quantified in BALF. Diagnostic utility was determined for biomarkers individually and in combination. RESULTS: Paired BALF culture and biomarker results were available for 150 patients. 53 patients (35%) had VAP and 97 (65%) patients formed the non-VAP group. All biomarkers were significantly higher in the VAP group (p<0.001). The area under the receiver operator characteristic curve for IL-1β was 0.81; IL-8, 0.74; MMP-8, 0.76; MMP-9, 0.79 and HNE, 0.78. A combination of IL-1β and IL-8, at the optimal cut-point, excluded VAP with a sensitivity of 100%, a specificity of 44.3% and a post-test probability of 0% (95% CI 0% to 9.2%). CONCLUSIONS: Low BALF IL-1β in combination with IL-8 confidently excludes VAP and could form a rapid biomarker-based rule-out test, with the potential to improve antibiotic stewardship

Policies for new path development: the case of Oxfordshire

This chapter reflects on how evolutionary economic geography (EEG) can be extended to incorporate public policy in its explanations of path development. A weakness of EEG is the poor conceptualisation of the role of the state (central, regional, local) in regional path development. It is therefore argued that a multi-scalar perspective of policy is required and that a large set of policies deserve attention. Oxfordshire in the UK is used to explore the link between public policy and path development

Extraction of small signal equivalent circuit model parameters for statistical modeling of HBT using artificial neural

We found different performances for the same device due to the variations in the process from die to the other on the same wafer or on another one. Yield analysis becomes one of the important tools into commercial Computer Aided Design (CAD) programs. Statistical issues are crucial in yield analysis for microwave circuits. Yield analysis needs accurate statistical properties between the parameters of devices’ models to reflect correctly the physical variations. Normally, on the level of the device modeling, the statistical properties between the model parameters like means and standard deviations are noisy by using the known techniques (optimization-based and direct) for extracting the small signal equivalent circuit model parameters of active microwave devices. We introduce how is Artificial Neural Network (ANN)accurate and efficient statistical extraction method for small signal model parameters of Hetero Junction Bipolar Transistor (HBT). Utilizing this methodology provides a robust statistical model for our device