Plane-Pair PEEC Model for Power Distribution Networks With Sub-Meshing Techniques

Wei, Leihao; Li, Liang; Shringarpure, Ketan; Ruehli, Albert E.; Wheeler, Edward; Fan, Jun; Archambeault, Bruce; Drewniak, James L.

doi:10.1109/tmtt.2016.2518164

Cited by 28 publications

(7 citation statements)

References 19 publications

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“…where 𝑁 𝑑𝑒𝑐𝑎𝑝 represents the number of used decaps, namely, the non-zero element in the array 𝑉 1 , 𝑉 2 , … , 𝑉 𝑁 . Full-wave simulations or some fast numerical algorithms [21], [22], [23] can be adopted to obtain the Z-parameter matrix of a PDN, where the ports of the Z-parameter are defined at the IC observation port and the decap locations. Then, the PDN impedance after placing decaps can be calculated using the Zparameter matrix by a segmentation method [24] as '1…”

Section: A Problem Definition Of Decap Optimizationmentioning

confidence: 99%

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

Jiang,

Zhang,

Tan

et al. 2024

IEEE Trans. Microwave Theory Techn.

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This article proposes a new physics-assisted genetic algorithm (PAGA) for decoupling capacitor (decap) optimization in power distribution networks (PDNs), which is, by far, the most powerful approach to efficiently minimizing the number of decaps within an enormous search space. In the proposed PAGA method, the priority of the decap ports is first determined based on their physical loop inductances. Then, an initial solution is quickly obtained by placing decaps sequentially on the port with the highest priority. Subsequently, a GA with prior physical knowledge is developed to find better decap solutions progressively. A port removal scheme that eliminates the lowpriority ports and a modified mutation operation that better guides the mutation direction is proposed to accelerate the convergence of the GA. The initial solution and the proposed auxiliary schemes can significantly narrow the search space and incorporate physical knowledge into the GA, thus greatly accelerating the convergence process. Several representative stateof-the-art algorithms and commercial tools are thoroughly compared with the proposed PAGA in different application scenarios. The proposed PAGA shows remarkably better performance in efficiently finding high-quality decap solutions and exhibits strong robustness to handle real-world and largescale problems, which brings decap optimization algorithms to a new benchmark.

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Section: A Problem Definition Of Decap Optimizationmentioning

confidence: 99%

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

Jiang,

Zhang,

Tan

et al. 2024

IEEE Trans. Microwave Theory Techn.

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“…Since then, it has been developed through a series of remarkable developments over the years, which have made it more robust and reliable [8]. Indeed, it has been used to model EM problems in various fields including power plane-pair [9], [10], decoupling capacitors [11], [12], PCB prelayout power integrity analysis [13], power modules [14], [15], on-chip interconnections [16], shielding [17], and antennas [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Anisotropic and Optimized FFT-Based Iterative Electromagnetic Solver for the PEEC Method

Romano,

Kovacevic-Badstuebner,

Antonini

et al. 2024

IEEE Trans. Microwave Theory Techn.

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Fast Fourier transform (FFT)-accelerated integralequation-based electromagnetic (EM) simulators have gained attraction for their capability to compute parasitics of arbitrarily shaped and large-scale voxelized structures on a desktop computer. Yet, FFT-based solvers have limitations due to the necessity of using voxels of the same size in all three Cartesian dimensions and suffer in the case of geometries with far-apart objects that require meshing also the air between them, resulting in a huge number of voxels and, thus, of unknowns. This work aims to overcome both these limitations by developing a systematic anisotropic strategy to compute matrix-vector products using the FFT-based approach and to remove the air existing between objects without sacrificing the desirable features of the FFT-based approach. The proposed approach is presented in the framework of the partial element equivalent circuit (PEEC) method, but it is well suited to be used also with other integral equationbased methods. The accuracy, efficiency, and applicability of the proposed anisotropic and optimized FFT (aoFFT)-based PEEC solver are demonstrated in the example of two structures requiring to use voxels of different sizes along the three Cartesian dimensions and with a large portion of air between the objects.

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“…Different methodologies have been proposed to model PDN structure and compute impedance. [1][2][3][4][5] The cavity model method 1,2 is an efficient approach to calculate PDN impedance, but it is limited to rectangular board shapes. The plane-pair partial element equivalent circuit (PEEC) method 3 can address irregular board shapes but requires solving a 2D mesh circuit and is, therefore, computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5] The cavity model method 1,2 is an efficient approach to calculate PDN impedance, but it is limited to rectangular board shapes. The plane-pair partial element equivalent circuit (PEEC) method 3 can address irregular board shapes but requires solving a 2D mesh circuit and is, therefore, computationally intensive. There are also some boundary integral methods 4,5 that only require 1D integration but are still not efficient enough in some applications.…”

Section: Introductionmentioning

confidence: 99%

“…A DNN can be trained by using a considerable number of boards with different configuration parameters. Compared to the traditional ways of calculating PDN impedance, [1][2][3][4][5] the trained DNN can be much faster while retaining a tolerable accuracy. Therefore, it can be a particularly powerful and efficient tool for PDN impedance evaluation at the design stage.…”

Section: Introductionmentioning

confidence: 99%

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Fast impedance prediction for power distribution network using deep learning

Zhang

Juang

Kiguradze

et al. 2021

Int J Numerical Modelling

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Modeling and simulating a power distribution network (PDN) for printed circuit boards with irregular board shapes and multi-layer stackup is computationally inefficient using full-wave simulations. This paper presents a new concept of using deep learning for PDN impedance prediction. A boundary element method (BEM) is applied to efficiently calculate the impedance for arbitrary board shape and stackup. Then over one million boards with different shapes, stackup, integrated circuits (IC) location, and decap placement are randomly generated to train a deep neural network (DNN). The trained DNN can predict the impedance accurately for new board configurations that have not been used for training. The consumed time using the trained DNN is only 0.1 s, which is over 100 times faster than the BEM method and 10 000 times faster than full-wave simulations.

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Plane-Pair PEEC Model for Power Distribution Networks With Sub-Meshing Techniques

Cited by 28 publications

References 19 publications

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

Anisotropic and Optimized FFT-Based Iterative Electromagnetic Solver for the PEEC Method

Fast impedance prediction for power distribution network using deep learning

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