1,720,965 research outputs found
Modeling for the Computer-Aided Design of Long Interconnects
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Machine Learning-Based Uncertainty Quantification of Passive Intermodulation in Aluminum Contact
Full text linkThis paper deals with the development of a surrogate model for the uncertainty quantification and the stochastic analysis of passive intermodulation (PIM) in an Aluminum-Aluminum contact based on the least-squares support vector machine (LS-SVM) regression. Starting from a small set of training pairs collecting the configuration of the un-certain parameters and the corresponding PIM level, the LS-SVM allows to build a closed-form approximation of such non-linear relationship. Such model, can be suitably used within a Monte Carlo (MC) scenario in order to accelerate the simulation process and provide all the statistical quantities of interest. The results show a considerable speed-up on the computational time compared to a plain MC simulation, while achieving an accurate approximation of the PIM probability density function
Bayesian Optimization of Hyperparameters in Kernel-Based Delay Rational Models
Full text linkThis paper presents an automatic procedure for the optimization of the hyperparameters of a delay rational model approximating the frequency-domain behavior of high-speed interconnects. The proposed model is built via a kernel-based regression, such as the Least-Square Support Vector Machine (LS-SVM), by considering an ad-hoc kernel with two hyperparameters related to the propagation delays introduced by the system. Such hyperparameters, along with the Tikhonov regularizer used by the LS-SVM regression, are carefully tuned via an automatic approach based on a k-fold cross-validation and Bayesian optimization. The feasibility of the effectiveness of the proposed modeling approach are investigated on a high-speed link
Machine Learning Applied to the Blind Identification of Multiple Delays in Distributed Systems
Full text linkThis paper focuses on the application of the Least-Square Support Vector Machine (LS-SVM) regression for the modeling of frequency responses of complex interconnect structures. The goal is to obtain a delayed-rational model (DRM) for the structure accounting for multiple time-delays generated by wave propagation and reflections along the channel.
A novel approach for the time-delays estimation based on the LS-SVM regression is introduced. The delays are estimated using the dual space formulation of the LS-SVM with an ad-hoc kernel that considers a possible delay interval.
The results highlight the lower order of DRMs obtained using the delays identified through this method when comparing to the vector fitting approach by applying it to a high-speed cable link
Multiple Delay Identification in Long Interconnects via LS-SVM Regression
Full text linkThis work presents a novel approach for the accurate estimation of multiple time-delays from the frequency response of a distributed system. The proposed approach is based on a powerful and flexible machine learning technique, namely, the least-square support vector machine (LS-SVM). The LS-SVM regression is used to construct a metamodel of the transfer function describing a generic linear time-invariant system in a delayed-rational form. Specifically, after some manipulation the LS-SVM model precisely identifies the dominant propagation delays of the original system. The essential steps and critical criteria for the delay identification procedure are carefully discussed throughout the paper. Once the system delays have been identified, the rational part of the metamodel expansion is then obtained by means of a progressive application of the conventional vector fitting algorithm. Numerical examples are presented to illustrate the feasibility and performance of the proposed technique and to compare its performances with what is provided by state-of-the-art techniques. The results clearly highlight the capability of the proposed approach to identify the dominant delays in distributed systems, thus allowing to construct compact delayed rational models
Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects
Full text linkIn this article, machine learning (ML) metamodels have been developed in order to predict the per-unit-length parameters of hybrid copper–graphene on-chip interconnects based on their structural geometry and layout. ML metamodels within the context of this article include artificial neural networks, support vector machines (SVMs), and least-square SVMs. The salient feature of all these ML metamodels is that they exploit the prior knowledge of the p.u.l. parameters of the interconnects obtained from cheap empirical models to reduce the number of expensive full-wave electromagnetic (EM) simulations required to extract the training data. Thus, the proposed ML metamodels are referred to as prior knowledge-based machine learning (PKBML) metamodels. The PKBML metamodels offer the same accuracy as conventional ML metamodels trained exclusively by full-wave EM solver data, but at the expense of far smaller training time costs. In this article, detailed comparative analysis of the proposed PKBML metamodels have been performed using multiple numerical examples
Sensitivity Analysis of Passive Intermodulation Due to Electrical Contacts
Full text linkNonlinear phenomena in electrical contacts deteriorate the quality of communication systems with the production of passive intermodulation (PIM). The theoretical evaluation of PIM as a function of the physical parameters of the contact is rather complicated. Standard linear and macroscopic contact models do not take into account all microscopic aspects of the contact responsible for its nonlinear behavior. For the above reason, an accurate analysis of the PIM should be carried out by using microscopic contact models, defined by dozens of parameters, some of which cannot be precisely measured or estimated. This article presents a statistical analysis of the PIM level by taking into account a possible uncertain interval for the physical parameters of the contact. Such statistical interpretation is then used in order to identify the most relevant physical parameters for the PIM generation via a sensitivity analysis, through the use of a surrogate model that speeds up the huge amount of PIM computations. The results of the sensitivity analysis allow us to build a simpler model depending only on few dominant parameters
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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