Power Integrity analysis and discrete optimization of decoupling capacitors on high speed power planes by particle swarm optimization

A novel EBG structure with super-wideband suppression of simultaneous switching noise in high speed circuits

Abstract: A novel uniplanar electromagnetic band-gap structure to maintain power integrity by suppressing simultaneous switching noise (SSN) is presented. The EBG structure with stopband from 750 MHz to 5.10 GHz is designed, fabricated and validated using network analyzer. Simulation results are verified by measurements and compared with the earlier published structures. Suppression of resonant cavity modes of power plane by EBG structure is also shown. The adoption of EBG structure in power deliver network is recommended to reduce the high frequency noise coupling between neighboring devices. These structures further help in better EMI/EMC compliance of the product by attenuating the propagation of high frequency noise between devices. The EBG structure usage can be on board, package or at die level.

Decoupling network optimization in high speed systems by mixed-integer programming

Abstract: Power Integrity is maintained in a high speed system by designing an efficient decoupling network This paper provides a generic formulation for decoupling capacitor selection and placement problem which is solved by mixed-integer programming A real-world example is presented for the same The minimum number of capacitors that could achieve the target impedance over the desired frequency range are found along with their optimal locations In order to solve an industrial problem, the s-parameters data of power plane geometry and capacitors are used for the accurate analysis including bulk capacitors and VRM

Application of nature inspired algorithms in power delivery network design: An industrial case study

Abstract: This paper presents application of nature inspired algorithms for solving an industrial problem of power delivery network design. To achieve the target impedance in a power delivery network, optimum number of decoupling capacitors and their optimum locations on board are found from thousands of available capacitors. S-parameters based data is used for the accurate analysis. Three natural computing algorithms - Particle Swarm Optimization, Cuckoo Search and Firefly Algorithm are used and compared for solving this problem.

A novel EBG power plane structure for suppressing SSN in high speed systems

Abstract: A novel uniplanar electromagnetic band-gap structure to maintain power integrity by suppressing simultaneous switching noise is presented. The proposed EBG structure is having stop-band from 600 MHz to 5.3 GHz and can be used as power plane in high speed systems for minimizing the power noise. Suppression of resonant cavity modes of power plane by EBG structure is shown. Simulation results are verified by measurements and compared with the earlier published structures.

The particle swarm - explosion, stability, and convergence in a multidimensional complex space

Abstract: The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.

Nature-Inspired Metaheuristic Algorithms

Abstract: Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.

Engineering Optimization: An Introduction with Metaheuristic Applications

Abstract: An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms. The author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts: Foundations of Optimization and Algorithms provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method Metaheuristic Algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search Applications outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization Throughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB and Octave software packages, and a related FTP site houses MATLAB code and programs for easy implementation of the discussed techniques. In addition, references to the current literature enable readers to investigate individual algorithms and methods in greater detail. Engineering Optimization: An Introduction with Metaheuristic Applications is an excellent book for courses on optimization and computer simulation at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners working in the fields of mathematics, engineering, computer science, operations research, and management science who use metaheuristic algorithms to solve problems in their everyday work.

Power Integrity Modeling and Design for Semiconductors and Systems

Abstract: The First Comprehensive, Example-Rich Guide to Power Integrity ModelingProfessionals such as signal integrity engineers, package designers, and system architects need to thoroughly understand signal and power integrity issues in order to successfully design packages and boards for high speed systems Now, for the first time, there's a complete guide to power integrity modeling: everything you need to know, from the basics through the state of the artUsing realistic case studies and downloadable software examples, two leading experts demonstrate today's best techniques for designing and modeling interconnects to efficiently distribute power and minimize noiseThe authors carefully introduce the core concepts of power distribution design, systematically present and compare leading techniques for modeling noise, and link these techniques to specific applications Their many examples range from the simplest (using analytical equations to compute power supply noise) through complex system-level applicationsThe authors Introduce power delivery network components, analysis, high-frequency measurement, and modeling requirements Thoroughly explain modeling of power/ground planes, including plane behavior, lumped modeling, distributed circuit-based approaches, and much more Offer in-depth coverage of simultaneous switching noise, including modeling for return currents using time- and frequency-domain analysis Introduce several leading time-domain simulation methods, such as macromodeling, and discuss their advantages and disadvantages Present the application of the modeling methods on several advanced case studies that include high-speed servers, high-speed differential signaling, chip package analysis, materials characterization, embedded decoupling capacitors, and electromagnetic bandgap structures This book's system-level focus and practical examples will make it indispensable for every student and professional concerned with power integrity, including electrical engineers, system designers, signal integrity engineers, and materials scientists It will also be valuable to developers building software that helps to analyze high-speed systems

Analog circuit design optimization through the particle swarm optimization technique

Abstract: This paper details the Particle Swarm Optimization (PSO) technique for the optimal design of analog circuits. It is shown the practical suitability of PSO to solve both mono-objective and multiobjective discrete optimization problems. Two application examples are presented: maximizing the voltage gain of a low noise amplifier for the UMTS standard and computing the Pareto front of a bi-objective problem, maximizing the high current cut off frequency and minimizing the parasitic input resistance of a second generation current conveyor. The aptness of PSO to optimize difficult circuit problems, in terms of numbers of parameters and constraints, is shown.