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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.


Papers
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DOI
22 May 2022
TL;DR: An efficient and fast Machine Learning based surrogate-assisted metaheuristic approach is proposed for the decoupling capacitor optimization problem to reduce the cumulative impedance of the PDN below the target impedance.
Abstract: Decoupling capacitors are commonly used in the design and optimization of Power Delivery Networks (PDNs) in high-speed very large scale integration systems (VLSI) to minimize the variations in the power supply and to maintain a low PDN ratio. In this paper, an efficient and fast Machine Learning (ML) based surrogate-assisted metaheuristic approach is proposed for the decoupling capacitor optimization problem to reduce the cumulative impedance of the PDN below the target impedance. The performance comparison of the proposed approach with state-of-the-art approaches is also presented.

2 citations

Journal ArticleDOI
10 Sep 2021-Sensors
TL;DR: In this article, a neural-network based nonlinear behavioral modeling of I/O buffer that accounts for timing distortion introduced by nonlinear switching behavior of the predriver electrical circuit under power and ground supply voltage (PGSV) variations is presented.
Abstract: This paper presents a neural-network based nonlinear behavioral modelling of I/O buffer that accounts for timing distortion introduced by nonlinear switching behavior of the predriver electrical circuit under power and ground supply voltage (PGSV) variations. Model structure and I/O device characterization along with extraction procedure were described. The last stage of the I/O buffer is modelled as nonlinear current-voltage (I-V) and capacitance voltage (C-V) functions capturing the nonlinear dynamic impedances of the pull-up and pull-down transistors. The mathematical model structure of the predriver was derived from the analysis of the large-signal electrical circuit switching behavior. Accordingly, a generic and surrogate multilayer neural network (NN) structure was considered in this work. Timing series data which reflects the nonlinear switching behavior of the multistage predriver’s circuit PGSV variations, were used to train the NN model. The proposed model was implemented in the time-domain solver and validated against the reference transistor level (TL) model and the state-of-the-art input-output buffer information specification (IBIS) behavioral model under different scenarios. The analysis of jitter was performed using the eye diagrams plotted at different metrics values.

2 citations

Proceedings ArticleDOI
22 May 2018
TL;DR: A semi-analytical method is presented to estimate the PSIJ in the presence of both the transmission media as well as the ground bounce, and while providing comparable accuracy yields significant speed-up.
Abstract: Power supply induced jitter (PSIJ) is becoming increasingly critical in modern high-speed and lower-power designs. In this paper, a semi-analytical method is presented to estimate the PSIJ in the presence of both the transmission media as well as the ground bounce. For this purpose, recently developed EMPSIJ method is extended to include the effects of both the ground bounce and the transmission line discontinuities. Results are presented by considering a voltage mode driver circuit and are compared against the conventional simulations (commercial tools) in a 55nm technology of STMicroelectronics. The new method while providing comparable accuracy yields significant speed-up.

2 citations

Proceedings ArticleDOI
01 Dec 2018
TL;DR: In this article, the authors present an analysis of jitter in a CMOS inverter due to power supply, ground bounce and substrate noise, and the results match reasonably well with mean percentage error (MPE) not exceeding 10%.
Abstract: This paper presents an analysis of jitter in a CMOS inverter due to power supply, ground bounce and substrate noise. The analysis is based on the recently introduced method EMPSIJ [1] which is extended in this paper for substrate noise induced jitter. To estimate jitter due to various noise sources (such as supply noise, ground bounce, substrate noise), noise transfer functions are derived. The results of EMPSIJ and the EDA simulations are compared for an inverter designed in a 28nm CMOS Technology of TSMC. For multiple test cases, the results match reasonably well with mean percentage error (MPE) not exceeding 10%.

2 citations

Proceedings ArticleDOI
26 Jul 2021
TL;DR: In this paper, the performance of different stochastic techniques based on Stochastic Collocation (SC) for UQ was evaluated for an illustrative example of a 2.4 GHz CMOS LC oscillator.
Abstract: In recent years, stochastic techniques have emerged as computationally superior techniques for Uncertainty Quantification (UQ). This paper focuses on the application of different stochastic techniques based on Stochastic Collocation (SC) for UQ. Here, the performance of different SC approaches like interpolation, regression and pseudo-spectral projection is assessed for an illustrative example of a 2.4 GHz CMOS LC oscillator. The application of these approaches for the oscillator circuit is investigated by performing the UQ of its phase noise output. The approaches are further compared with the traditional Monte Carlo simulations. The advantages and disadvantages of each of the methods clearly emerge from our study that helps in choosing the appropriate technique for modeling the uncertainty for any given similar oscillator circuit.

1 citations


Cited by
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Journal Article
TL;DR: 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.
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

13,333 citations

01 Jan 2016
TL;DR: The logical effort designing fast cmos circuits is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can download it instantly.
Abstract: Thank you for reading logical effort designing fast cmos circuits. As you may know, people have search numerous times for their chosen novels like this logical effort designing fast cmos circuits, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some harmful bugs inside their desktop computer. logical effort designing fast cmos circuits is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the logical effort designing fast cmos circuits is universally compatible with any devices to read.

137 citations

Journal ArticleDOI
TL;DR: An adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed that can effectively avoid the premature convergence problem and the convergence rate is faster.
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.

74 citations

Journal ArticleDOI
TL;DR: A general form of PSO algorithms is considered, and asymptotic properties of the algorithms using stochastic approximation methods are analyzed, proving that a suitably scaled sequence of swarms converge to the solution of an ordinary differential equation.
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.

45 citations