Pedro Pereira

This paper introduces a tool for the optimization of CMOS integrated spiral inductors. Its aim is to offer designers a first approach on designing inductors without the need for fabrication. The core of the tool is an optimization procedure where technology constraints on the inductor layout parameters are considered by applying user-defined discretization on the design variables. User-defined constraints between layout parameters may also be accounted for, as a way of taking into account design heuristics. For those cases where the device area is a major concern area minimization may be considered. On the other hand, if a major design goal is the inductor quality factor the tool may yield the layout parameters which maximize the quality factor. The trade-off between quality factor and device area is evaluated through the generation of a graphical representation of quality factor versus output diameter for a given inductance. For the sake of simplicity the pi-model has been used for characterising the inductor. The application was developed in Matlab and the optimization toolbox is used. The validity of the design results obtained is checked against circuit simulation with ASITIC.

Power Quality is a generic term focusing on several issues, going from reliability to the quality of service provided by the energy supplier. It addresses limiting aspects such as harmonic distortion, flicker, sags, swells and transients... It is important for the students to understand the differences between the large amounts of events that fit into poor power quality category. Moreover it is important for them to analyze real time non-laboratory events. To provide this experience to the students this paper presents a remote m-learning experimental system where several types of poor power quality events can be tested. The developed system is based on a power quality analysis distributed network and can be remotely accessed from a remote computer or smart phones.

This paper describes the development of an open source system for monitoring and data acquisition of several energy analyzers. The developed system is based on a computer with Internet/Intranet connection by means of RS485 using Modbus RTU as communication protocol. The monitoring/metering system was developed for large building complexes and was validated in the Faculdade de Ciências e Tecnologia University campus. The system considers two distinct applications. The first one allows the user to verify, in real time, the energy consumption of any department in the complex, produce load diagrams, tables and print, email or save all available data. The second application keeps records of active/reactive energy consumption in order to verify the existence of some anomalous situation, and also monthly charge energy consumption to each corresponding department.

This work introduces a tool for the optimization of CMOS integrated spiral inductors. The main objective of this tool is to offer designers a first approach for the determination of the inductor layout parameters. The core of the tool is a Genetic Algorithm (GA) optimization procedure where technology constraints on the inductor layout parameters are considered. Further constraints regarding inductor design heuristics are also accounted for. Since the layout parameters are inherently discrete due to technology and topology constraints, discrete variable optimization techniques are used. The Matlab GA toolbox is used and the modifications on the GA functions, yielding technology feasible solutions is presented. For the sake of efficiency and simplicity the pi-model is used for characterizing the inductor. The validity of the design results obtained with the tool, is checked against circuit simulation with ASITIC.

The progressive scaling of CMOS technology towards nanometre sizes has made the implementation of highly integrated systems for the wireless communication systems possible. Additionally, higher speed, lower power consumption and area reduction has been reached. Due to the high-density integration needs, as well as to low cost fabrication, RF applications, such as the LC-voltage controlled oscillator (LC-VCO), are usually implemented in CMOS technology. The complexity of designing LC-VCOs has lead to the development of several design methodologies. This chapter introduces an optimization based methodology for the design of LC-VCOs, where its efficiency is granted by the use of analytical models to characterize the active and passive elements’ behaviour.

The continuing size reduction of electronic devices imposes design challenges to optimize the performances of modern electronic systems, such as: wireless services, telecom and mobile computing. Fortunately, those design challenges can be overcome thanks to the development of Electronic Design Automation (EDA) tools. In the analog, mixed signal and radio-frequency (AMS/RF) domains, circuit optimization tools have demonstrated their usefulness in addressing design problems taking into account downscaling technological aspects. Recent advances in EDA have shown that the simulation-based sizing technique is a very interesting solution to the ‘complex’ modelling task in the circuit design optimization problem. In this paper we propose a multi-objective simulation-based optimization tool. A CMOS LC-VCO circuit is presented to show the viability of this tool. The tool is used to generate the Pareto front linking two conflicting objectives, namely the VCO Phase Noise and Power Consumption. The accuracy of the results is checked against HSPICE/RF simulations.

This paper introduces a variability-aware methodology for the design of LC-VCOs in Nano-CMOS technologies. The complexity of the design as well as the necessity for having an environment offering the possibility for exploring design trade-offs has led to the development of design methodologies based multi-objective optimization procedures yielding the generation of Pareto-optimal surfaces. The efficiency of the process is granted by using analytical models for both passive and active devices. Although physics-based analytical expressions have been proposed for the evaluation of the lumped elements, the variability of the process parameters is usually ignored due to the difficulty to formalize it into an optimization performance index. The usually adopted methodology of considering only optimum solutions for the Pareto surface, may lead to pruning quasi-optimal solutions that may prove to be better, should their sensitivity to process parameter variation be accounted for. In this work we propose starting by generating an extended Pareto surface where both optimum and quasi-optimum solutions are considered. Finally information on the sensitivity to process parameter variations, is used for electing the best design solution.

This paper presents a remote laboratory linked with mobile devices for real data analysis on the field of power quality. A global system was developed from the power quality analyzer into the human machine interface devoted to the m-learning system. This m-learning system is intended to be used in a long life learning perspective. The developed remote laboratory is a good opportunity for people, even without deep knowledge on the field, to learn power quality principles in an applied way. Since the system is based on real data, is a good approach to give trainees practical knowledge on the field.

This paper addresses an optimization-based approach for the design of RF integrated inductors. The methodology presented deals with the complexity of the design problem by formulating it as a multi-objective optimization. The multi-modal nature of the underlying functions combined with the need to be able to explore design trade-offs leads to the use of niching methods. This allows exploring not only the best trade-off solutions lying on the Pareto-optimum surface but also the quasi-optimum solutions that would be otherwise discarded. In this paper we take advantage of the niching properties of lbest PSO algorithm using ring topology to devise a simple optimizer able to find the local-optima. For the efficiency of the process analytical models are used for the passive/active devices. In spite the use of physics-based analytical expressions for the evaluation of the lumped elements, the variability of the process parameters is ignored in the optimization stage due to the significant computational burden it involves. Thus in the final stage both the Pareto-optimum solutions and the quasi-optimum solutions are evaluated with respect to the sensitivity to process parameter variations.

High temperature superconducting (HTS) machines are recognized to offer several advantageous features when comparing to conventional ones. Amongst these, highlights the decrease in weight and volume of the machines, due to increased current density in conductors or the absence of iron slots' teeth; or the decrease in AC losses and consequent higher efficiency of the machines, even accounting for cryogenics. These concepts have been already demonstrated and some machines have even achieved commercial stage. In this paper, several alternative approaches are applied to electrical motors employing HTS materials. The first one is an all superconducting linear motor, where copper conductors and permanent magnets are replaced by Bi-2223 windings and trapped flux magnets, taking advantage of stable levitation due to flux pinning, higher current densities and higher excitation field. The second is an induction disk motor with Bi-2223 armature, where iron, ironless and hybrid approaches are compared. Finally, an innovative command strategy, consisting of an electronically variable pole pairs' number approach, is applied to a superconducting hysteresis disk motor. All these concepts are being investigated and simulation and experimental results are presented.

In this paper an optimization-based approach for the design of RF integrated inductors is addressed. For the characterisation of the inductor behaviour the double pi-model is used. The use of this model is twofold. On one hand it enables the generation of the inductor characterisation in a few seconds. On the other hand its integration into the optimization procedure is straightforward. For the evaluation of the model element values analytical expressions based on technology parameters as well as on the device geometric characteristics are used. The use of a technology-based methodology for the evaluation of the model parameters grants the adaptability of the model to any technology. The inductor analytical characterization is integrated into an optimization-based tool for the automatic design of RF integrated inductors. This tool uses a modified genetic algorithm (MGA) optimization procedure, which has proved its validation in previous work. Due to the design parameter constraints nature as well as the topology constraints, discrete variables optimization techniques are used. The accuracy of the results is checked against a non-commercial software.

In this paper, three metaheuristics are investigated to optimize RF Integrated Inductors; namely Differential Evolution (DE) from the field of evolutionary computation, Gravitational Search Algorithm (GSA) based on the law of gravity and mass interactions and Particle Swarm Optimization (PSO) inspired by swarm behaviors in nature. A particular interest is given to the optimization of RF Integrated Inductors. Performances in terms of optimum quality and computing time of the metaheuristics are checked via three test functions and one application that consist of optimizing performances of characterize integrated inductors based on the double$π$-model.

The sand pile model, in conjunction with Bean model, is often applied to describe single grain bulk superconductors. However, in several applications such as electric motors, multiseeded bulks are needed, due to the need to increase sample dimensions. In this paper, an extension of the sand pile model is presented in order to manage this type of materials. Multiseeded HTS bulk superconductors, produced, e.g., by the top-seeded melt growth process, are characterized by intra- and intergrain currents, and these are reflected in the model. However, identifying these currents from flux density measurements is not straightforward, when considering more than one grain. In fact, the number of currents increases with the number of grains, and these have to be identified from the measured field surface. A method to identify these currents based on genetic algorithms is validated with artificial data and then used in real measurements.

The high prices currently achieved in the acquisition of non-renewable energy for electricity production and the low levels of energy efficiency in the housing sector are the national situation, which leads the Portuguese government to encourage the acquisition, the installation and the use of technologies which exploit indigenous and renewable energy. This study presents an application that was developed in order to help the citizen in his decision to invest in renewable technologies in their homes. The application is able to elaborate an economic analysis based on the selected type of renewable technology, providing the user with the knowledge of benefits and the annual costs involved in the system that he selected. This tool aims at facilitating the interaction of any user with such technologies and it can be used as a helpful tool to support the decision of investment in such systems.

The complex data exchange between architectural design and building energy simulation constitutes the main challenge in the use of energy performance analyses in the early design stage. The enhancement of BIM model data with additional specific energy-related information and the subsequent mapping to the input of an energy analysis or simulation tool is yet an open issue. This paper examines three approaches for the data transfer from 3D CAD applications to building performance simulations using BIM as central data repository and points out their current and envisaged use in practice. The first approach addresses design scenarios. It focuses on the supporting tools needed to achieve interoperability given a 74 wide-spread commercial BIM model (Autodesk Revit) and a dedicated pre-processing tool (DesignBuilder) for EnergyPlus. The second approach is similar but addresses retrofitting scenarios. In both workflows gbXML is used as the transformation format. In the third approach a standard BIM model, IFC is used as basis for the transfer process for any relevant lifecycle phase.

This Chapter presents the optimal design of radio-frequency integrated spiral inductors. The basic idea is to generate an analytical model to characterize integrated inductors based on the double {\ensuremathπ}-model, and offer to the designer an approach to determine the inductor layout parameters. Particle Swarm Optimization technique is used to generate optimal values of parameters of the developed models. Viability of the proposed models is highlighted via comparison with ASITIC simulation results.

In this paper a discrete-variable optimization methodology for the automatic design of CMOS integrated spiral inductors is introduced. The use of discrete variable optimization procedure offers the designer the possibility for exploring the design space exclusively in those points available for the technology under use. Further user-defined constraints between layout parameters may also be incorporated as a way of taking into account design heuristics. A comparison between using discrete-variable optimization and a continuous optimization procedure followed by a discretization of the results is presented, where the benefits of the proposed methodology are presented. An application using the proposed methodology was developed in Matlab and the optimization toolbox is used. For the sake of simplicity the pi-model has been used for characterizing the inductor. The validity of the design results is checked against circuit simulation with ASITIC.

The advancement of CMOS technology led to the integration of more complex functions. In the particular of wireless transceivers, integrated LC tanks are becoming popular both for VCOs and integrated filters [1]. For RF applications the main challenge is still the design of integrated inductors with the maximum quality factor. For that purpose, tapered, i.e., variable width inductors have been proposed in the literature. In this paper, analytical expressions for the determination the pi-model parameters, for the characterization of variable width integrated inductors are proposed. The expressions rely exclusively on geometrical and technological parameters, thus granting the rapid adaptation of the model to different technologies. The results obtained with the model are compared against simulation with ASITIC, showing errors below 10%. The model is then integrated into an optimization procedure where inductors with a quality factor improvement in the order of 20-30% are obtained, when compared with fixed width inductors.

Sand pile and Bean models have already been applied to describe single grain HTS bulks. An extension to that approach was used to model multiseed bulks, needed for several practical applications as electric motors or flywheels with superconducting bearings. The use of genetic algorithms was then proposed to determine intra- and intergrain current densities, and application to two and three seeds samples using trapped flux experimental measurements was exemplified. However, this model assumed some simplifications, as equal properties in grain boundaries between neighboring grains. In this paper an extension to this methodology is proposed and evaluated by analyzing measurements performed in plans at different distances from surfaces of samples with three seeds. Discussion of its influence on a practical application is also explored.

Progressive scaling of CMOS technology towards nanoscale regime enables the design of highly integrated systems for the wireless communications market. As technology continues to scale, the variability in process parameters may cause significant deviations in device behaviour. The complexity of designing spiral inductors has lead to the development of multi-objective optimization based design methodologies yielding the generation of Pareto-optimal surfaces. However, the variability of the process parameters is usually ignored, yielding the selection of ideally optimal solutions in detriment of quasi-optimal solutions that may prove to be better, should the robustness against process parameter variation be accounted for. We propose the generation of an extended Pareto front containing both optima and quasi-optima solutions. Finally information on the robustness to process parameter variations, is used for electing the best design solutions.The evaluation of the extended set of sub-optima solutions requires methods capable to find the set of local optima, since solutions that are close to each other in the performance index space may be very distant in the design parameter space.

This work describes the development of a system designed for renewable power generation integration. It continuously acquires wind, solar and temperature data, which is automatically correlated with energy parameters, obtained from renewable energy systems. The developed system was installed in an urban building equipped with photovoltaic cells and wind renewable generation. To validate the developed application, it was analyzed data of a wind generator and a set of photovoltaic panels, installed near to the weather station. The developed application allows, in addition to the acquisition of weather and energy data, their continuous monitoring and correlation through a graphical user interface, providing a friendly interactivity with the user.