Machine Learning based Surrogate Modeling of Electronic Devices and Circuits

L'abstract è presente nell'allegato / the abstract is in the attachmen

Vector-Valued Kernel Ridge Regression for the Modeling of High-Speed Links

This paper presents a preliminary implementation of a general modeling framework for vector-valued functions based on a multi-output kernel Ridge regression (KRR). The proposed approach is based on a generalized definition of the reproducing kernel Hilbert space (RKHS) to the case of vector-valued functions, thus bridging the gap between multi-output Neural Network (NN) structures and standard scalar kernel-based approaches. The above concept is then used within the KRR to train a multi-output surrogate model able to predict the frequency responses of a high-speed link affected by four parameters with a large variability. The performance of the proposed approach, in terms of parametric and stochastic analysis, is compared with the one provided by two state-of-the-art techniques, such as the combination of the principal components analysis (PCA) and the least-squares support vector machine (LS-SVM) regression and a multi-output feed-forward NN structure

Efficient Implementation of the Vector-Valued Kernel Ridge Regression for the Uncertainty Quantification of the Scattering Parameters of a 2-GHz Low-Noise Amplifier

This paper focuses on the application of an efficient implementation of the vector-valued kernel Ridge regression (KRR) to the uncertainty quantification (UQ) of the scattering parameters of a low-noise amplifier (LNA). Specifically, the performance of the proposed technique have been investigated for the statistical assessment of the mean value, variance and probability density function (PDF) of the S11 and S21 parameters of a 2-GHz LNA induced by 25 stochastic input parameters and compared with the corresponding reference results computed via a plain Monte Carlo (MC) simulation

Bridging the Gap Between Artificial Neural Networks and Kernel Regressions for Vector-Valued Problems in Microwave Applications

Thanks to their convex formulation, kernel regressions have shown an improved accuracy with respect to artificial neural network (ANN) structures in regression problems where a reduced set of training samples are available. However, despite the above interesting features, kernel regressions are inherently less flexible than ANN structures since their implementations are usually limited to scalar-output regression problems. This article presents a vector-valued (multioutput) formulation of the kernel ridge regression (KRR) aimed at bridging the gap between multioutput ANN structures and scalar kernel-based approaches. The proposed vector-valued KRR relies on a generalized definition of the reproducing kernel Hilbert space (RKHS) and on a new multioutput kernel structure. The mathematical background of the proposed vector-valued formulation is extensively discussed together with different matrix kernel functions and training schemes. Moreover, a compression strategy based on the Nystrom approximation is presented to reduce the computational complexity of the model training. The effectiveness and the performance of the proposed vector-valued KRR are discussed on an illustrative example consisting of a high-speed link and on the optimization of a Doherty amplifier

Crosstalk analysis of multi‐microstrip coupled lines using transmission line modeling

This article presents a new analytical method to predict crosstalk of a homogeneous terminated two microstrip coupled lines over a ground plane using transmission line theory. The derived formula is frequency and location dependent, which can be used to quickly estimate the crosstalk of a coupled line. Also, the effect of the geometrical parameters of the lines and load are included in the derived formula. Presented method can be used for the other types of coupled lines including lossy or lossless lines. To verify the accuracy of the introduced method, a few microstrip coupled line structures with different geometrical parameters are considered numerically and experimentally. The results of crosstalk based on the proposed analytical methods, simulation study using high frequency structure simulator and those obtained by measurements are reported and compared with each other. It is shown that our proposed method accurately estimates the amount of crosstalk for a two microstrip coupled lines

Crosstalk analysis at near-end and far-end of the coupled transmission lines based on eigenvector decomposition

In this paper, a new analytical method is proposed to accurately estimate the near-end and far-end crosstalk of a coupled Transmission Lines (TLs) based on eigenvector decomposition. For a non-homogenous two coupled lines, the related linear differential equations system (LDES) is derived for distributed voltage and current and then using matrix analysis, its four distinct eigenvalues and their associated eigenvectors are determined. It is shown that the two eigenvalues represent the self-propagation constant, while the other ones are linked to the mutual propagation constant of the coupled lines. In addition to, for these lines a closed form expression for near-end and far-end crosstalk is presented. In special case of homogenous coupled lines, the LDES is also determined and it is shown that they provide two couples of eigenvalues. Using the concept of generalized eigenvalues, the solution of these systems is derived and a closed form formula is derived for crosstalk. In order to verify the accuracy of the proposed method a few types of coupled lines, including homogeneous or non-homogeneous are investigated and the amount of crosstalk is estimated. The calculated crosstalk is presented and compared with those obtained by numerical investigation. It is shown that a good agreement is obtained between the calculated and measured results

Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects

In 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

A comparison of effectiveness of regulation of working memory function and methylphenidate on remediation of attention deficit hyperactivity disorder (ADHD).

Abstract: Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent and serious disorder affecting such key cognitive components as working memory. Working memory serves to facilitate and check attention in any individual and to focus on those affairs that need to be retained in mind. This study examines whether a combination of the two therapeutic methods of working memory training and Methylphenidate might be more effective in treating ADHD in children aged 6 to 12 years of age than when methylphenidate is applied alone. Method: Subjects of the study are 48 children suffering from ADHD. They were selected by random sampling. The experimental group included 23 children with ADHD who received a combination of working memory training and Methylphenidate, and the control group which included 25 children with ADHD received Methylphenidate only. To check the effects of the intervention, Conners' Parent Rating Scale (CPRS-48) was applied before and after the intervention. After intervention, data were collect d from the remaining samples in the two groups. Data were examined both through descriptive statistical methods and analytic statistical methods, including T-student test and Quantile-Quantile Plots diagram . Results: The study demonstrated that a combination of the cognitive intervention of working memory training and methylphenidate is more effective in alleviating ADHD symptoms rather than when methylphenidate is applied in isolation. In the CPRS pre-test and post-test, the mean difference of the experimental and the control group was 8.39 and 1.88 respectively, indicating that the working memory group has improved more than the control group. Conclusions: The study reveals that the ADHD symptoms were more contained in the test group than the control group due to working memory training . The cognitive intervention through working memory training may be effective in alleviating the severity of disorder measured in the pre-test

A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement

Background: Low intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation (tACS or tDCS), applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional magnetic resonance imaging (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. Objective: To develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency, and reproducibility (ContES Checklist). Methods: A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists (EP) through the International Network of the tES-fMRI (INTF) Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC based on a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed using the checklist. Results: Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (1) technological factors, (2) safety and noise tests, and (3) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article. Conclusions: Use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies, and increase methodological transparency and reproducibility