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.

Robust optimization of serial link system for signal integrity and power integrity

Abstract: Signal Integrity (SI) and Power Integrity (PI) are the most critical issues for higher operational speeds in semiconductor industry. This work identifies and optimizes the parameters of board, package and termination environment, influencing the signal integrity and power integrity of serial link. System level model has been created for USB HSLINK taking into account the external parameters like board, package, measurement environment which influence the performance of the channel. Parameters variations appearing from manufactura-bility constraints, material property constraints, design tolerance etc affecting the serial link performance has been optimized using Taguchi method based on statistical co-analysis. Using the Taguchi statistical techniques, sensitivity analysis on parameter variations affecting HSLINK performance is analyzed and optimized for desired performance.

Mitigating the impact of sinusoidal jitter and duty cycle distortion on random jitter estimation by Tailfit algorithm

Abstract: Random jitter (RJ) estimation based on Tail Fit algorithm are generally inaccurate in presence of deterministic components like the presence of sinusoidal jitter (SJ) and duty cycle distortion (DCD). Addition of deterministic jitter changes the standard deviation of the tail region of resulting jitter probability density function. A new methodology for random jitter estimation in presence of sinusoidal jitter and duty cycle distortion is presented. The method involves calculation of mathematical correction factor, which are derived and used to calculate the precise value of random jitter. The proposed methodology is validated by inducing known jitter sources in real channel simulator of ADS from Agilent.

A Thomas Algorithm-Based Generic Approach for Modeling of Power Supply Induced Jitter in CMOS Buffers

Abstract: This paper presents an efficient and generic method for analysis of power supply induced jitter (PSIJ) in a chain of CMOS inverters as well as tapered buffers due to multiple deterministic noise sources. Generalised semi-analytical relations between noise and PSIJ are developed using Thomas algorithm. The proposed analysis can be used for both cases of same size of inverters as well as tapered buffers, and also for considering the effect of on-chip and off-chip interconnects. The validity and the efficiency of the proposed modeling is demonstrated for various applications of chain of inverters such as buffers in clock distribution, delay locked loops and I/Os, etc.

Uncertainty Quantification of Memristor Crossbar Array for Vector Matrix Multiplication

Abstract: This work focuses on the study of variability analysis of a memristor-based crossbar. The memristor crossbars are particularly useful in neuromorphic circuits due to their high power efficiency and low latency. This paper presents a variability analysis of the R off to R on ratio due to the change in the parameters of the crossbar cell. A single cell in the crossbar consists of one transistor one memristor (1T1M) based structure. The Zewail-city memristor model is used in the crossbar cell for the analysis. This paper also presents the analysis of the 1T1M structure and 1T1M based crossbar read-write operations. For variability analysis, a stochastic technique named Polynomial Chaos is used and the results are compared with the standard Monte Carlo simulations.

Deterministic Noise Analysis for Single-Stage Amplifiers by Extension of Indefinite Admittance Matrix

Abstract: This article presents two methods, the block approach indefinite admittance matrix (BA-IAM) and the estimation-by-inspection, to analyse the effects of deterministic noise on single-stage, single-ended amplifiers by extending the indefinite admittance matrix. The proposed methods are used to develop a generalised two-port network analysis for the commonly used amplifier topologies, in the presence of the supply, ground, bulk, and input noise sources. Various illustrative case studies (common-source, common-gate, and push-pull amplifiers) are considered to validate the analytical method of different CMOS technology nodes (180 nm, 110 nm, and 28 nm) and foundries (Lfoundry, UMC, and TSMC). Both the proposed methods are compared with the relevant existing methods in terms of mean percentage error (MPE), and computational complexity. The mathematically derived expressions using two methods show less than 4% MPE when compared with the schematic simulation results, obtained by the SPICE based simulations. Also, the post-layout simulations (PLS) results for all the examples (designed in CMOS 180 nm Lfoundry technology) show excellent matching with schematic simulations. The proposed methods can be further applicable to antennas, complex circuits, digital circuits, etc.

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.