This paper proposes an advanced technique to develop combined neural network and pole-residue-based transfer function models for parametric modeling of electromagnetic (EM) behavior microwave components. In this technique, networks are trained learn the relationship between pole/residues functions geometrical parameters. The order pole-residue may vary over different regions We a tracking solve order-changing problem. After proposed process, model can be used provide accurate fast prediction...

This article introduces the deep neural network method into field of high-dimensional microwave modeling. Deep learning is nowadays highly successful in solving complex and challenging pattern recognition classification problems. investigates use networks to solve modeling problems that are much more than solved by previous shallow networks. The most commonly used activation function existing rectified linear unit (ReLU), which a piecewise hard switch function. However, such ReLU not...

This article presents an overview of artificial neural network (ANN) techniques for a microwave computer-aided design (CAD). ANN-based are becoming useful performing forward/inverse modeling active/passive components to enhance circuit design. With measured or simulated data devices, ANNs can be trained learn relevant relationships, which are, otherwise, computationally expensive efficient analytical formulas not available. Fundamental concepts the ANN structure and training, such as...

Space mapping is a recognized method for speeding up electromagnetic (EM) optimization. Existing space-mapping approaches belong to the class of surrogate-based optimization methods. This paper proposes cognition-driven formulation space that does not require explicit surrogates. The proposed applied EM-based filter new technique utilizes two sets intermediate feature parameters, including frequency parameters and ripple height parameters. design variables are mapped which further By...

he prevalence of smart devices has promoted the popular- ity mobile applications (a.k.a. apps) in recent years. A number interesting and important questions remain unan- swered, such as why a user likes/dislikes an app, how app becomes popular or eventually perishes, selects apps to install interacts with them, frequently is used much traffic it generates, etc. This paper presents empirical analysis usage behaviors collected from millions users Wandoujia, leading An- droid marketplace China....

Yield-driven optimization is important in microwave design due to the uncertainties introduced manufacturing process. For first time, we extend this paper use of polynomial chaos (PC) approach from electromagnetic (EM)-based yield estimation EM-based structures. We formulate a novel objective function for yield-driven EM optimization. By incorporating PC coefficients into formulation, analytically related variables, which are nominal point. then derive sensitivity formulas with respect...

Space mapping (SM) is a recognized method for speeding up electromagnetic (EM) optimization. The SM technique often requires an equivalent circuit as the coarse model. In practical cases, models are not always available. This letter addresses this situation and proposes new coarse- fine-mesh EM optimization incorporating mesh deformation. By deformation into coarse-mesh optimization, responses change continuously values of geometrical design variables change. incorporation also improves...

This paper proposes a pole-residue-based adjoint neuro-transfer function (neuro-TF) technique with electromagnetic (EM) sensitivity analysis for parametric modeling of EM behavior microwave components respect to changes in geometrical parameters. The purpose is increase model accuracy by utilizing information and speed up development reducing the number training data required developing model. proposed consists original pole-residue based neuro-TF models. New formulations are derived...

Space mapping (SM) is a recognized method for speeding up electromagnetic (EM) optimization. Existing SM approaches are mostly based on sequential computation mechanism. This paper proposes parallel EM In the proposed method, surrogate model developed in each iteration trained to match fine at multiple points simultaneously. Multi-point training and enables be valid larger neighborhood than that standard SM. The formulation of multi-point inherently suited implemented through computation....

This paper proposes a novel technique to develop low-cost electromagnetic (EM) centric multiphysics parametric model for microwave components. In the proposed method, we use space mapping techniques combine computational efficiency of EM single physics (EM only) simulation with accuracy simulation. The responses respect different values geometrical parameters in nondeformed structures without considering other domains are regarded as coarse model. is developed using modeling methods such...

This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed addresses the situation where response of starting point is far away from design specifications. We propose to utilize multiple feature parameters help move passband into range pole–zero-based neuro-TF introduced in this extract when responses are not explicitly identified....

Knowledge-based neural network modeling techniques using space-mapping concept have been demonstrated in the existing literature as efficient methods to overcome accuracy limitations of empirical/equivalent circuit models when matching new electromagnetic data. For different problems, mapping structures can be different. In this paper, we propose a unified automated model generation algorithm that uses l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Feature-based electromagnetic (EM) optimization techniques can help avoid local minima in microwave design. Zeros of the transfer functions are recently used to extract features when filter responses not explicitly identifiable. This letter proposes a feature zero-adaptation approach enlarge surrogate range by overcoming problem varying orders function w.r.t. changes design variables. In this way, proposed technique allows larger step sizes for optimization, therefore, speeding up overall EM...

Artificial neural networks (ANNs) are information processing systems, with their design inspired by studies of the ability human brain to learn from observations and generalize abstraction. Researchers have investigated a variety important applications utilizing ANNs perform modeling optimization microwave components circuits, such as high-speed very large-scale integration (VLSI) interconnects [1]-[3], spiral inductors [4], field-effect transistors (FETs) [5], [6], heterojunction bipolar...

Artificial neural network technique has gained recognition as a powerful in microwave modeling and design. This paper proposes novel deep topology for parametric of components. In the proposed network, outputs are S-parameters. The inputs model include geometrical variables frequency. We divide hidden layers into two parts. Hidden Part I handle both frequency while II only inputs. this way, more training parameters utilized to specifically learn relationship between S-parameters variables,...

This article proposes a novel parallel gradient-based electromagnetic (EM) optimization approach to microwave components using adjoint-sensitivity-based neuro-transfer function (neuro-TF) surrogate. In the proposed technique, surrogate model is trained not only input-output behavior but also adjoint sensitivity information generated from EM simulation simultaneously. By exploiting for modeling, technique can obtain accurate models with larger valid range same amount of fine evaluations...

Abstract Inverse modeling of microwave components plays an important role in design and diagnosis or tuning. Since the analytical function formula inverse input‐output relationship does not exist is difficult to obtain, artificial neural network (ANN) becomes efficient tool develop models for components. This paper provides overview recent advances network‐based techniques applications. We review two different shallow techniques, including comprehensive methodology multivalued technique....

Space mapping is a recognized surrogate-based optimization method to accelerate the electromagnetic (EM) design. In this article, for first time, space elevated from solving problem of EM multiphysics high power microwave filters. Multiphysics analysis, which involves domain with other physics domains, increasingly important high-performance components obtain an accurate system To speed up design, space-mapping-based surrogate model including coarse and two functions proposed in article. We...

Neuro-transfer function (neuro-TF) methods are deemed as powerful tools in modeling the electromagnetic (EM) behavior of microwave passive components. Existing neuro-TF either endure issue &#x201C;order-changing&#x201D; or mismatch poles/zeros, both calling for specific algorithms to process data transfer coefficients a part model development. This letter proposes novel method eliminate two issues simultaneously by using combined neural networks and model-order reduction (MOR)-based rational...

Metasurfaces find a wide variety of applications in the last decades due to their powerful ability manipulate electromagnetic (EM) waves. Traditional approaches for metasurface design require massive full-wave EM simulations achieve optimal geometrical parameter values, resulting an inefficient process metasurfaces. In this article, we propose physics-driven machine-learning (ML) approach incorporating temporal coupled mode theory (CMT) improve efficiency and implement intelligent proposed...