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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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Journal ArticleDOI
TL;DR: In this paper , an analytical approach is presented which estimates jitter in CMOS inverters in the presence of power supply noise (PSN), data noise (DN), and ground-bounce noise (GBN) by deriving analytical relationships.
Abstract: With the advancement of semiconductor technology (enabling the dimensions of the switching devices in the range of nanometer scale) designing, modeling, and optimization of high-speed circuits are becoming very complicated. Various issues related to signal and power integrity come into picture at high-frequency operations, e.g., jitter, cross-talk, electromagnetic interference, etc. In this article, an analysis of the CMOS inverter in presence of deterministic noise is presented. An analytical approach is presented which estimates jitter in CMOS inverters in the presence of power supply noise (PSN), data noise (DN), and ground-bounce noise (GBN) by deriving analytical relationships. The proposed analytical method takes into account the device parameters to model timing uncertainty. The expression for jitter is obtained by estimating the deviation of each transition edge from its ideal position. Several examples (simulations as well as measurement) are presented to validate the proposed modeling. These examples include comparing the analytical results with the simulation results obtained using an SPICE-based simulator as well as doing the same with the experimental results using two different CMOS inverter integrated circuits (ICs). In order to test the independence of the proposed modeling approach on a specific technology node, the results are verified by considering different technology nodes such as: 40 nm, 65 nm, and 180 nm from United Microelectronics Corporation. Also, two different ICs (M74HC04, and MC74AC04 N) from different vendors are used for measurement. The results obtained using the proposed methodology are in close consonance with those obtained from simulations using the SPICE-based simulator and the experiments.

1 citations

Proceedings ArticleDOI
24 Oct 2022
TL;DR: In this paper , a methodology for blood pressure estimation from Electrocardiograph data using Machine Learning (ML) techniques is proposed that can be run on resource-constrained devices e.g., wearable devices.
Abstract: Edge computing allows the analysis of data close to the sources of its generation. This computing paradigm has enabled multiple avenues in different types of applications with the usage of Artificial Intelligence. Smart remote health monitoring is one such application that requires medical data analysis with the help of AI. In this paper, a methodology for blood pressure estimation from Electrocardiograph data using Machine Learning (ML) techniques is proposed that can be run on resource-constrained devices e.g., wearable devices. The proposed methodology requires only ECG data that can be acquired in a non-invasive manner. Experimental results show that the proposed methodology is able to achieve better results compared to similar techniques proposed in the literature.

1 citations

Proceedings ArticleDOI
17 May 2020
TL;DR: In this paper, a generalised indefinite admittance matrix (GIAM) method is proposed to formulate these metrics for active circuits, including the impact of deterministic supply noise on performance parameters.
Abstract: The presence of fluctuations in the power and ground supply rails affect the quality of various performance metrics (gain, phase, impedance, transient response, etc.) of any analog and mixed-signal system. In this paper, a generalised indefinite admittance matrix (GIAM) method is proposed to formulate these metrics for active circuits. The proposed analysis includes the impact of deterministic supply noise on performance parameters. For the purpose of analysis, a common source amplifier, designed in a standard CMOS 180 nm technology, is analysed. The maximum mean percentage error between analytical results and Cadence® Virtuoso® tool simulations for the amplifier is less than 4 % for all the cases.

1 citations

Proceedings ArticleDOI
01 Dec 2018
TL;DR: A close matching is seen between the results obtained by proposed and conventional methods with a significant speedup reported.
Abstract: A nonlinear analysis of a DC-DC buck converter is presented in context of its harmonics in a power delivery network (PDN). The closed-form relationships of the harmonic distortions associated with the DC-DC buck converter are derived by Volterra series to estimate the effects in a practical environment using S-parameters. For the validation of the proposed method, the buck converter is designed in a 180 nm LDMOS technology and three test cases are considered by using three different practical PDNs. A close matching is seen between the results obtained by proposed and conventional methods with a significant speedup reported.

1 citations

DOI
TL;DR: In this article , a knowledge-based artificial neural network (ANN) is developed for predicting jitter in CMOS inverter circuits in the presence of power supply noise (PSN), which provides for efficient training in a hybrid approach using input data extracted from both analytical closed-form expressions and a circuit simulator.
Abstract: In this article, a knowledge-based artificial neural network (ANN) is developed for predicting jitter in CMOS inverter circuits in the presence of power supply noise (PSN). The proposed ANN provides for efficient training in a hybrid approach using input data extracted from both analytical closed-form expressions and a circuit simulator. The proposed ANN demonstrates a reasonably accurate prediction of power supply-induced jitter (PSIJ) with results that closely match that from directly using a circuit simulator (HSPICE) for a case study with 22-nm CMOS technology.

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