Submitted by Sandro Camargo (sandro.camargo@unipampa.edu.br) on 2015-05-09T19:09:43Z No. of bitstreams: 1 117110018.pdf: 4311604 bytes, checksum: 0978b40aef931c296de315514d7d64ac (MD5) / Made available in DSpace on 2015-05-09T19:09:43Z (GMT). No. of bitstreams: 1 117110018.pdf: 4311604 bytes, checksum: 0978b40aef931c296de315514d7d64ac (MD5) Previous issue date: 2012-11-22 / A indústria de microeletrônica tem a sua evolução ditada pela necessidade cada vez maior de integração de circuitos como memórias e processadores, fazendo com que os dispositivos semicondutores sejam cada vez mais miniaturizados. Esta miniaturização implica processos de fabricação cada vez mais complexos, resultando em uma grande variabilidade de parâmetros. O projeto de circuitos analógicos torna-se cada vez mais complexo, pois em geral é altamente suscetível às variações de processo, o que afeta a sua produtividade. Uma das partes mais complexas deste projeto é o dimensionamento dos dispositivos que compõem o circuito, pois o espaço de projeto é altamente não-linear e nem sempre se conhece a localização do seu ponto ótimo. Neste contexto, este trabalho tem como objetivo o desenvolvimento de uma ferramenta para o dimensionamento automático de circuitos integrados analógicos, capaz de lidar com a variabilidade dos parâmetros e visando aumentar a produtividade do circuito gerado. Esta ferramenta baseia-se no dimensionamento do circuito como um problema de otimização baseado em simulação elétrica SPICE. O objetivo principal é receber as especificações requeridas de uma topologia de circuito e, através de técnicas de inteligência artificial, explorar o espaço de soluções em busca de soluções otimizadas que atendam às restrições impostas. Além disso, espera se obter soluções que atendam às especificações requeridas mesmo com variações no processo de fabricação. Para isso, são empregadas técnicas de design centering de modo a maximizar a produtividade do circuito. A ferramenta desenvolvida foi implementada de maneira modular, permitindo que a análise do dimensionamento do circuito possa ser realizada sob diferentes aspectos. Como resultado, este trabalho apresenta duas topologias de amplificadores operacionais automaticamente dimensionadas em tecnologia CMOS, tendo como objetivo a minimização da área de gate e da potência dissipada, além da maximização da produtividade. Os circuitos gerados apresentaram melhor desempenho em comparação com resultados descritos na literatura. / The microelectronics industry has the CMOS technology evolution dictated by the capability of integration of digital circuits such as memories and processors, causing the semiconductor devices miniaturization. The miniaturization leads to complex manufacturing processes with high parameters variation. Analog circuit designs are complex and highly susceptible to process variations, affecting the circuit yield. One of the most complex part of the analog design is the circuit sizing, since the possible solutions have a highly nonlinear design space and the optimal solution is not known. In this context, this work aims at developing a tool for the automatic sizing of analog integrated circuits that is able to deal with parameter variation in order to yield maximization. This tool is based on the circuit sizing as an optimization problem based on electrical SPICE simulations. The main objective is to receive the required specifications of a circuit topology and, by means of artificial intelligence techniques, to explore the design space for optimized solutions that meet the circuit constraints. Furthermore, it is expected to obtain solutions which meet the specifications required even with the presence of variations in the manufacturing process. For this purpose, design centering techniques are implemented for yield maximization. The tool is implemented with modular functions, enabling the sizing process on different configurations. As results, this work present the automatic design of two CMOS operational amplifiers topologies, with the goal to reduce the power dissipation and the gate area and to maximize the yield. The results present good performance when compared to similar designs found in literature.

Millimeter-wave has been a medium for automotive, sensor, and defense applications for a long time. But, a fully integrated silicon-based transceiver at 60 GHz or higher frequencies has become the driving force for recent research activities in integrated millimeter-wave (MMW) circuit designs. However, no integrated compact high-performance millimeter-wave system can be designed without accurate estimation and optimization of layout parasitics. In this dissertation, the estimation, modeling and optimization of parasitic effects as well as the verification of extraction methodologies for RF/MMW applications are investigated. Different circuit design- and layout-examples are considered with stress on the inclusion and optimization of wire/interconnect parasitics. A novel methodology is proposed to reduce the number of design-passes and to include layout parasitics in the design optimization procedure. An automated verification procedure for existing parasitic extraction tools is developed. Neural-network-based models are used to demonstrate the effectiveness of artificial intelligence techniques for characterizing parasitic components. The parasitic sensitivities for selected millimeter-wave circuits are demonstrated, and a parasitic benchmarking procedure is developed using MMW oscillators. Measurement results of several circuits that are implemented in state-of-the-art CMOS and SiGe-BiCMOS processes are used to demonstrate the role of parasitics and the systematic design methodology including parasitics.

No description available.

Die Konstruktion von Systemen oder Modellen, welche unter Unsicherheit und Umweltschwankungen robust arbeiten, ist eine zentrale Herausforderung sowohl im Ingenieurwesen als auch in den Naturwissenschaften. Dies ist im Design-Zentrierungsproblem formalisiert als das Finden eines Designs, welches vorgegebene Spezifikationen erfüllt und dies mit einer hohen Wahrscheinlichkeit auch noch tut, wenn die Systemparameter oder die Spezifikationen zufällig schwanken. Das Finden des Zentrums wird oft durch das Problem der Quantifizierung der Robustheit eines Systems begleitet. Hier stellen wir eine neue adaptive statistische Methode vor, um beide Probleme gleichzeitig zu lösen. Unsere Methode, Lp-Adaptation, ist durch Robustheit in biologischen Systemen und durch randomisierte Lösungen für konvexe Volumenberechnung inspiriert. Lp-Adaptation ist in der Lage, beide Probleme im allgemeinen, nicht-konvexen Fall und bei niedrigen Rechenkosten zu lösen. In dieser Arbeit beschreiben wir die Konzepte des Algorithmus und seine einzelnen Schritte. Wir testen ihn dann anhand bekannter Vergleichsfälle und zeigen seine Anwendbarkeit in elektronischen und biologischen Systemen. In allen Fällen übertrifft das vorliegende Verfahren den bisherigen Stand der Technik. Dies ermöglicht die Umformulierung von Optimierungsproblemen im Ingenieurwesen und in der Biologie als Design-Zentrierungsprobleme unter Berücksichtigung der globalen Robustheit des Systems. / The design of systems or models that work robustly under uncertainty and environmental fluctuations is a key challenge in both engineering and science. This is formalized in the design centering problem, defined as finding a design that fulfills given specifications and has a high probability of still doing so if the system parameters or the specifications randomly fluctuate. Design centering is often accompanied by the problem of quantifying the robustness of a system. Here we present a novel adaptive statistical method to simultaneously address both problems. Our method, Lp-Adaptation, is inspired by how robustness evolves in biological systems and by randomized schemes for convex volume computation. It is able to address both problems in the general, non-convex case and at low computational cost. In this thesis, we describe the concepts of the algorithm and detail its steps. We then test it on known benchmarks, and demonstrate its real-world applicability in electronic and biological systems. In all cases, the present method outperforms the previous state of the art. This enables re-formulating optimization problems in engineering and biology as design centering problems, taking global system robustness into account.

Die Konstruktion von Systemen oder Modellen, welche unter Unsicherheit und Umweltschwankungen robust arbeiten, ist eine zentrale Herausforderung sowohl im Ingenieurwesen als auch in den Naturwissenschaften. Dies ist im Design-Zentrierungsproblem formalisiert als das Finden eines Designs, welches vorgegebene Spezifikationen erfüllt und dies mit einer hohen Wahrscheinlichkeit auch noch tut, wenn die Systemparameter oder die Spezifikationen zufällig schwanken. Das Finden des Zentrums wird oft durch das Problem der Quantifizierung der Robustheit eines Systems begleitet. Hier stellen wir eine neue adaptive statistische Methode vor, um beide Probleme gleichzeitig zu lösen. Unsere Methode, Lp-Adaptation, ist durch Robustheit in biologischen Systemen und durch randomisierte Lösungen für konvexe Volumenberechnung inspiriert. Lp-Adaptation ist in der Lage, beide Probleme im allgemeinen, nicht-konvexen Fall und bei niedrigen Rechenkosten zu lösen. In dieser Arbeit beschreiben wir die Konzepte des Algorithmus und seine einzelnen Schritte. Wir testen ihn dann anhand bekannter Vergleichsfälle und zeigen seine Anwendbarkeit in elektronischen und biologischen Systemen. In allen Fällen übertrifft das vorliegende Verfahren den bisherigen Stand der Technik. Dies ermöglicht die Umformulierung von Optimierungsproblemen im Ingenieurwesen und in der Biologie als Design-Zentrierungsprobleme unter Berücksichtigung der globalen Robustheit des Systems. / The design of systems or models that work robustly under uncertainty and environmental fluctuations is a key challenge in both engineering and science. This is formalized in the design centering problem, defined as finding a design that fulfills given specifications and has a high probability of still doing so if the system parameters or the specifications randomly fluctuate. Design centering is often accompanied by the problem of quantifying the robustness of a system. Here we present a novel adaptive statistical method to simultaneously address both problems. Our method, Lp-Adaptation, is inspired by how robustness evolves in biological systems and by randomized schemes for convex volume computation. It is able to address both problems in the general, non-convex case and at low computational cost. In this thesis, we describe the concepts of the algorithm and detail its steps. We then test it on known benchmarks, and demonstrate its real-world applicability in electronic and biological systems. In all cases, the present method outperforms the previous state of the art. This enables re-formulating optimization problems in engineering and biology as design centering problems, taking global system robustness into account.

In this thesis, a new approach combining neural networks and genetic algorithms is presented for microwave design. In this method, an accurate neural network model is developed from the experimental data. This neural network model is used to perform sensitivity analysis and derive response surfaces. An innovative technique is then applied in which genetic algorithms are coupled with the neural network model to assist in synthesis and optimization. The proposed method is used for modeling and analysis of circuit parameters for flip chip interconnects up to 35 GHz, as well as for design of multilayer inductors and capacitors at 1.9 GHz and 2.4 GHz. The method was also used to synthesize mm wave low pass filters in the range of 40-60 GHz. The devices obtained from layout parameters predicted by the neuro-genetic design method yielded electrical response close to the desired value (95% accuracy). The proposed method also implements a weighted priority scheme to account for tradeoffs in microwave design. This scheme was implemented to synthesize bandpass filters for 802.11a and HIPERLAN wireless LAN applications in the range of 5-6 GHz. This research also develops a novel neuro-genetic design centering methodology for yield enhancement and design for manufacturability of microwave devices and circuits. A neural network model is used to calculate yield using Monte Carlo methods. A genetic algorithm is then used for yield optimization. The proposed method has been used for yield enhancement of SiGe heterojunction bipolar transistor and mm wave voltage-controlled oscillator. It results in significant yield enhancement of the SiGe HBTs (from 25 % to 75 %) and VCOs (from 8 % to 85 %). The proposed method is can be extended for device, circuit, package, and system level integrated co-design since it can handle a large number of design variables without any assumptions about the component behavior. The proposed algorithm could be used by microwave community for design and optimization of microwave circuits and systems with greater accuracy while consuming less computational time.