Jai Narayan Tripathi

Bio: Jai Narayan Tripathi is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Jitter & Power integrity. The author has an hindex of 9, co-authored 65 publications receiving 247 citations. Previous affiliations of Jai Narayan Tripathi include STMicroelectronics & Indian Institutes of Technology.

Modeling the effects of transmission media on power supply induced jitter

Abstract: In this paper, a method is presented to estimate the effect of transmission media on power supply induced jitter for a voltage-mode driver circuit Transmission media is represented via its equivalent models of transmission lines while calculating the power supply induced jitter The proposed semi-analytical method for jitter analysis is compared against the conventional simulations (commercial tools) in a 55nm technology of STMicroelectronics A reasonable matching is reported

Metaheuristic Optimization of Decoupling Capacitors in a Power Delivery Network

Abstract: In VLSI circuits and systems, it is a common practice to reduce power supply noise in power delivery networks by decoupling capacitors. The optimal selection and placement of decoupling capacitors is crucial for maintaining power integrity efficiently. This paper presents a metaheuristic technique based generic framework for decoupling capacitor optimization in a practical power delivery network. The cumulative impedance of a power delivery network is minimized below the target impedance by optimal selection and placement of decoupling capacitors using state-of-the-art metaheuristic algorithms. A comparative analysis of the performance of these algorithms is presented with the insights of practical implementation.

Jitter Estimation in a CMOS Tapered Buffer for an Application of Clock Distribution Network

Abstract: This paper presents an analysis and estimation of timing error due to the power supply noise for a five-stage CMOS tapered buffer used in the clock distribution network for the application of successive approximation register (SAR). The complete design is simulated in standard 28 nm CMOS technology with the supply voltage of 0.9 V. The closed-form expressions for time interval error have been derived using the matrix inversion method, considering the impact of deterministic supply noise. The results obtained from a slope-based semianalytical jitter estimation method are compared with the SPICE based simulations. The results show a good agreement with a maximum error of 4 %.

Distortion analysis for a DC-DC buck converter

Abstract: This paper presents design and harmonic distortion analysis using Volterra series for a 12 V to 1.2 V buck converter designed in the 180 nm BCD8 technology of STMicroelectronics. The series determines the closed-form equations for fundamental, second and third harmonics. From the analysis, the results are 96 %, 97 % and 90 % matched with the simulation results when the amplitude of the input ripple varies from 0 V to 1.8 V.

An Automated Framework for Variability Analysis for Integrated Circuits Using Metaheuristics

Abstract: This work aims to analyze the variability of integrated circuits and systems. An automated framework is presented for variability analysis that exploits the metaheuristic optimization techniques. The efficacy of the proposed approach is demonstrated by two case studies—one is the estimation of variability in phase noise in RF CMOS LC tank oscillators (frequency domain analysis) and the other is the estimation of variability in the differential output signal of a current mode driver (time-domain analysis). The proposed approach is investigated and validated by comparing the results from the traditional Monte Carlo simulations and the ordinary least-squares-based polynomial chaos expansion. A significant gain in the computational time is reported while maintaining accuracy in the results. The proposed methodology is not just limited to variability analysis applications but also can be used to solve the circuit optimization problems.

The Design and Analysis of Experiments

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

Logical Effort Designing Fast Cmos Circuits

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An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network

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.

Analyzing Convergence and Rates of Convergence of Particle Swarm Optimization Algorithms Using Stochastic Approximation Methods

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.