Mário Ventim-Neves

ADISI- An efficient tool for the automatic design of integrated spiral inductors

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 π-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.

GADISI - Genetic Algorithms Applied to the Automatic Design of Integrated Spiral Inductors

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.

LC-VCO design methodology based on evolutionary algorithms

In his paper the design of LC-VCOs is addressed. Due to the high-density integration needs as well as to low cost fabrication, RF applications are usually implemented in CMOS technology. However, this technology development brought up several issues such as the degradation of on-chip LC tank quality factor, yielding VCOs phase noise limitation. To overcome phase-noise limitations, optimization design methodologies are usually used. Since electromagnetic simulations are timely expensive, model based approaches are needed. In this work the characterization of the oscillator behaviour is guaranteed by a set of analytical models describing each circuit element performance. A set of working examples for UMC130 technology, aiming the VCO phase noise and power consumption optimization, is addressed. The results presented, spotlight the potential of the proposed design methodology, combined with a GA optimization procedure, for an accurate and timely efficient oscillator design. The accuracy of the results is checked against HSPICE/RF simulator.

LC-VCO Design Challenges in the Nano-Era

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.

Optimization-Based Design of Nano-CMOS LC-VCOs

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.

Automatic generation of RF integrated inductors analytical characterization

This paper addresses the automatic generation of RF integrated inductors model. In this work the double p-model is used as a way of characterizing the inductor behaviour over a frequency range beyond the self-resonant value. 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 models generated 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 Genetic Algorithm (GA) optimization procedure, where user defined constraints on the design parameters are taken into account. Due to the design constraints nature and topology constraints, discrete variables optimization techniques are used.

Next Day Load Forecast: A Case Study for the City of Lisbon

Effective short-term load forecasting plays a crucial role in the operation of both traditional and deregulated power systems. Improving the accuracy of load forecasting can increase the appropriateness of planning and scheduling and reduce operational costs of power systems making them resemble resilient energy systems. In the present paper, we propose the regressive forecast model of the day ahead based on the artificial neural network. The electric load peaks were also calculated by the model. The data used were the time series of active power, recorded by EDP Distribution Telemetry System, collected in Lisbon. The results show that our approach provides a reliable model for forecast daily and hourly energy consumption, as well the load profile with accuracy.

Load forecasting as a computational tool to support smart grids

Load forecasting is a fundamental task for the planning, operation and exploration of the Electric Power Systems. The importance of forecasting has become more evident with the restructuring of the national energy sector and the creation of projects linked to smart grids, namely in Portugal — o InovGrid. This paper describes the computational forecast model of the Daily Load Diagram based on the Levenberg-Marquardt algorithm of Artificial Neural Networks. The data to be used are the time series of active power recorded by the EDP Distribution Telemetry System and the climatic time series of the Portuguese Institute of the Sea and Atmosphere, collected on the city of Évora, one of the first smart cities in Portugal. The forecast horizon is short term: from 24 hours to a week. The good results observed by the statistical error indicators were obtained.

Load Forecasting in Electrical Distribution Grid of Medium Voltage

The importance of forecasting has become more evident with the appearance of the open electricity market and the restructuring of the national energy sector. This paper presents a new approach to load forecasting in the medium voltage distribution network in Portugal. The forecast horizon is short term, from 24 h up to a week. The forecast method is based on the combined use of a regression model and artificial neural networks (ANN). The study was done with the time series of telemetry data of the DSO (EDP Distribution) and climatic records from IPMA (Portuguese Institute of Sea and Atmosphere), applied for the urban area of Evora - one of the first Smart Cities in Portugal. The performance of the proposed methodology is illustrated by graphical results and evaluated with statistical indicators. The error (MAPE) was lower than 5 %, meaning that chosen methodology clearly validate the feasibility of the test.

Load peak forecasting in different load patterns situations

Load forecast is becoming currently more fundamental in planning, operating and controlling of modern electric power systems. Nowadays the load peak forecast is also important, because it is of great interest in economy stability and improvement in the electrical systems. This paper presents an approach for load forecasting in the medium voltage distribution network in Portugal. The forecast horizon is short term, from 24 hours up to a week. The forecast method is based on the use of artificial neural networks. The study was done with the time series of telemetry data of EDP medium voltage substations, applied for the urban areas of Évora and Oporto. The performance of the proposed methodology is illustrated by graphical results and evaluated with statistical indicators. The mean absolute percentage error was lower than 5%, meaning that chosen methodology clearly validate the feasibility of the test.