Jai Narayan Tripathi

Abstract: The primary focus of this paper is to discuss the modeling of jitter caused by power supply noise (PSN), named power supply induced jitter (PSIJ). A holistic discussion is presented from the basics of power delivery networks to PSN and eventually to the modeling of PSIJ. The in-depth details and a review of several methodologies available in the literature for the estimation of PSIJ are presented.

Abstract: An efficient methodology for estimation of power supply induced jitter (PSIJ) in high-speed designs is presented. Semianalytical expressions for jitter are derived based on separating the large signal response and the small signal noise response and subsequently combining the results. Proposed simplified relations enable the designers to estimate the PSIJ based on a single bit simulation. Proposed methods are validated on several examples of voltage-mode driver circuits, designed in different technologies and in the presence of different types of noise sources.

Abstract: Estimation of jitter in early design cycle of an SoC is necessary to avoid jitter budget conflicts in the design. In this paper, an analysis of power supply induced jitter in a commonly used voltage mode driver architecture in serial links is discussed. The circuit used for the analysis is designed in 28nm FD-SOI technology but the analysis is technology independent. Jitter induced by noise in power delivery networks is analyzed by a transfer function from power supply to the output by a small signal equivalent model. The analysis can be extended generically for System-On-Chip (SoC) level design considerations.

Abstract: This paper presents stochastic convergence analysis of particle swarm optimization algorithm involving randomness and applying the results to the Analog RF Circuits to optimize the circuit parameters. In every iteration, each particle position is represented as vector and the standard particle swarm algorithm determined by positive real tipple {w,c 1 ,c 2 }. Comparisons for convergence are presented with respect to fixed tipple {w,c 1 ,c 2 } and random tipple {w,c 1 ,c 2 }. Various results show that the randomness in defining new position to particle leads to better convergence property. Also, exploration and exploitation trade off are discussed with examples. It is demonstrated that each particle undergoes both exploration and exploitation in convergence process; if the randomness in the particle generation is considered. The parameters considered for RF circuit are cutoff frequency, Phase Noise and Signal to Noise Ratio (SNR). Results are compared between both fixed values and random values of parameters in convergence analysis of PSO.

Abstract: Swarm intelligence is applied to a module of high speed system design problem. To maintain power integrity in a high speed system, an effective methodology for suppressing the cavity-mode anti-resonances' peaks is presented. The optimal values and the optimal positions of the decoupling capacitors are found using three different swarm intelligence methods - particle swarm optimization, cuckoo search method and firefly algorithm. Optimum values and locations of decoupling capacitors are obtained, by which anti-resonances' peaks of loaded board are minimized.

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

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Abstract: The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster.

Abstract: Recently, much progress has been made on particle swarm optimization (PSO). A number of works have been devoted to analyzing the convergence of the underlying algorithms. Nevertheless, in most cases, rather simplified hypotheses are used. For example, it often assumes that the swarm has only one particle. In addition, more often than not, the variables and the points of attraction are assumed to remain constant throughout the optimization process. In reality, such assumptions are often violated. Moreover, there are no rigorous rates of convergence results available to date for the particle swarm, to the best of our knowledge. In this paper, we consider a general form of PSO algorithms, and analyze asymptotic properties of the algorithms using stochastic approximation methods. We introduce four coefficients and rewrite the PSO procedure as a stochastic approximation type iterative algorithm. Then we analyze its convergence using weak convergence method. It is proved that a suitably scaled sequence of swarms converge to the solution of an ordinary differential equation. We also establish certain stability results. Moreover, convergence rates are ascertained by using weak convergence method. A centered and scaled sequence of the estimation errors is shown to have a diffusion limit.