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Concept# Small-signal model

Résumé

Small-signal modeling is a common analysis technique in electronics engineering used to approximate the behavior of electronic circuits containing nonlinear devices with linear equations. It is applicable to electronic circuits in which the AC signals (i.e., the time-varying currents and voltages in the circuit) are small relative to the DC bias currents and voltages. A small-signal model is an AC equivalent circuit in which the nonlinear circuit elements are replaced by linear elements whose values are given by the first-order (linear) approximation of their characteristic curve near the bias point.
Overview
Many of the electrical components used in simple electric circuits, such as resistors, inductors, and capacitors are linear. Circuits made with these components, called linear circuits, are governed by linear differential equations, and can be solved easily with powerful mathematical frequency domain methods such as the Laplace transform.
In contrast, many of th

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EE-203: Electronics II

Ce cours introduit les composants à semiconducteurs électroniques de base : diodes à jonction PN, transistors bipolaires et MOS. Leurs modes de fonctionnement en DC et AC sont étudiés. Les circuits élémentaires à base de transistors bipolaires sont présentés et analysés.

EE-332: Electronic circuits and systems II

L'étudiant maîtrise la conception et la mise en oeuvre des circuits et systèmes électroniques sous forme discrète et intégrée. L'accent est mis sur les applicationb dans le domaine des télécommunications.

EE-519: Bioelectronics and biomedical microelectronics

The course covers the fundaments of bioelectronics and integrated microelectronics for biomedical and implantable systems. Issues and trade-offs at the circuit and systems levels of invasive microelectronic systems as well as their eluding designs, methods and classical implementations are discussed

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Transistor bipolaire

vignette|Vue interne d'un transistor bipolaire de puissance 2N3055 conçu dans les années 1970.
vignette|Transistor bipolaire monté en surface.
Un transistor bipolaire est un dispositif électronique à

Quadripôle

En électrocinétique, un quadripôle (ou quadrupôle) est un élément de modèle d'un circuit électrique dans lequel on le considère comme un bloc avec deux connexions d'entrée et deux de sortie. On étudi

Transistor

vignette|Quelques modèles de transistors.
Le transistor est un composant électronique à semi-conducteur permettant de contrôler ou d'amplifier des tensions et des courants électriques. C'est le compo

Séances de cours associées (74)

DC-DC converters based on Application Specific Integrated Circuits (ASICs) have been developed in this doctoral work for the High-Luminosity Large Hadron Collider (HL-LHC) experiments at CERN. They step down the voltage from a 2.5 V line and supply a load current up to 3 A. The main focus has been the miniaturization of the converters while maintaining high efficiency, together with the improvement of the dynamic performance and the minimization of the impact of substrate parasitic devices. These are challenges that industry and research are facing to power modern microprocessors, with the aim of minimizing the system volume, cost and power consumption, while guaranteeing good regulation performance.
Miniaturization is a key requirement for application in the HL-LHC experiments, since any added material is detrimental for the physics performance. In addition, the converters must be tolerant to a high magnetic field (up to 4 T) and to ultra-high levels of radiation. Tolerance to the magnetic field is achieved by employing air-core inductors (which are the bulkiest components on the board), while radiation-hard ASICs have been designed in this work in a commercial 130 nm CMOS technology by extensively applying hardening by design techniques.
Converters using two different architectures have been designed: a buck converter, which had been identified in a previous work as a good option to achieve high efficiency and low mass, and a Resonant Switched-Capacitor (ReSC) converter, which can further increase the power density while keeping high efficiency.
A novel dual-edge pulse width modulator for the buck converter that has improved dynamic performance compared to conventional solutions has been designed and implemented. Its small-signal response has been modeled, and the model has been validated by measurements.
In addition, this thesis proposes an integrated system that monitors in real-time the voltage stress experienced by the devices of the buck converter and adjusts its operation accordingly, in order to guarantee the required reliability, while maximizing the efficiency. The main building block of this system has been designed and has proved to be fully functional.
A substrate-currents-aware characterization method that allows the evaluation of the impact of substrate parasitic devices on the circuit performance and reliability has been also devised, and it has been applied to select the best floor-plan for the buck converter.
A novel control scheme that optimizes the efficiency of the ReSC converter for the whole load current range by adopting different operation modes has been proposed and implemented. This thesis reports the steady-state analysis of each mode and a small-signal model for one of them.
The buck converter uses a 100 nH inductor, and the last prototype shows a peak efficiency of 88.4% for the 2.5 V-to-1.2 V conversion. It complies with the radiation specifications, and mass production will soon start.
The designed prototype ReSC converter employs a 12 nH inductor, and its area occupancy and thickness are respectively 20% and 55% lower than the buck. Its efficiency is larger than that of the buck converter in a range of conversion ratios and load currents, reaching a peak efficiency of 91.4% for the 2.5 V-to-1.2 V conversion. Radiation tests have highlighted that the converter complies with Total Ionizing Dose specifications. By introducing a few improvements, a future prototype could be close to production readiness.

Networked systems are composed of interconnected nodes that work collaboratively to maximize a given overall utility function. Typical examples of such systems are wireless sensor networks (WSNs) and participatory sensing systems: sensor nodes, either static or mobile, are deployed for monitoring a certain physical field. In these systems, there are a set of problems where we need to adaptively select a strategy to run the system, in order to enhance the efficiency of utilizing the resources available to the system. In particular, we study four adaptive selection problems as follows. We start by studying the problem of base-station (BS) selection in WSNs. Base stations are critical sensor nodes whose failures cause severe data losses. Deploying multiple fixed BSs improves the robustness, yet this scheme is not energy efficient because BSs have high energy consumptions. We propose a scheme that selects only one BS to be active at a time; other BSs are kept passive and act as regular sensor nodes. This scheme substantially reduces the energy supplies required by individual BSs. Then, we propose an algorithm for adaptively selecting the active BS so that the spatially and temporally varying energy resources are efficiently utilized. We also address implementation issues and apply the proposed algorithm on a real WSN. Field experiments have shown the effectiveness of the proposed algorithm. We generalize the BS selection problem by considering both the energy efficiency of regular sensor nodes and that of BSs. In this scheme, a subset of active BSs (instead of only one) is adaptively selected and the routing of regular sensor nodes is adjusted accordingly. Because BSs have high fixed-energy consumptions and because the number of candidate subsets of active BSs is exponential with the number of BSs, this general BS selection problem is NP-hard. We propose a polynomial-time algorithm that is guaranteed, under mild conditions, to achieve a network lifetime at least 62% of the optimal one. Through extensive numerical simulations, we verify that the lifetime achieved by the proposed algorithm is always very close to the optimum. We then study the problem of scheduling the sparse-sensing patterns in WSNs. We observe that the traditional scheme of periodically taking sensing samples is not energy efficient. Instead, we propose to adaptively schedule when and where to activate sensors for sampling a physical field, such that the energy efficiency is enhanced and the sensing precision is maintained. The schedules are learnt from the temporal signal models derived from the collected measurements. Then, using the obtained signal models and the sparse sensing-measurements, the original signal can be effectively recovered. This proposed method requires minimal on-board computation, no inter-node communications and achieves an appealing reconstruction performance. With experiments on real-world datasets, we demonstrate significant improvements over both traditional sensing schemes and the state-of-the-art sparse-sensing schemes, particularly when the measured data is characterized by a strong temporal correlation. In the last part of the thesis, we discuss the sparse-sensing framework by exploiting the spatial correlations rather than the temporal correlations among the captured measurements. In this framework, application-specific utility functions can be employed. By adaptively selecting a small subset of active sensors for sensing, a certain utility is guaranteed and the efficiency of the sensing system is enhanced. We apply this framework both in static WSNs and participatory sensing systems where sensors move in an uncoordinated manner. Through extensive simulations, we show that our proposed algorithm enhances the resource efficiency.

Maria-Anna Chalkiadaki, Yogesh Singh Chauhan, Christian Enz, Sourabh Khandelwal

BSIM6 is the latest industry-standard bulk MOSFET model from the BSIM group developed specially for accurate analog and RF circuit designs. The popular real-device effects have been brought from BSIM4. The model shows excellent source-drain symmetry during both dc and small signal analysis, thus giving excellent results during analog and RF circuit simulations, e.g., harmonic balance simulation. The model is fully scalable with geometry, biases, and temperature. The model has a physical charge-based capacitance model including polydepletion and quantum-mechanical effect thereby giving accurate results in small signal and transient simulations. The BSIM6 model has been extensively validated with industry data from 40-nm technology node.