Design Space Extrapolation for Power Delivery Networks using a Transposed Convolutional Net

Bhatti, Osama Waqar; Swaminathan, Madhavan

doi:10.1109/isqed51717.2021.9424309

Cited by 3 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These can dramatically accelerate design optimization and uncertainty quantification studies [11]. Applications of neural networks to microwave circuit design have focused on models based on input parameters that describe fixed geometries, such as the length of stubs in microstrip filters [12]. More recently, physics-informed neural networks for Maxwell's equations have been presented [13], aimed at solving simple electromagnetic wave interaction problems.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on Their Layouts

Sarris

2022

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

This paper demonstrates a deep learning based methodology for the rapid simulation of planar microwave circuits based on their layouts. We train convolutional neural networks to compute the scattering parameters of general, twoport circuits consisting of a metallization layer printed on a grounded dielectric substrate, by processing the metallization pattern along with the thickness and dielectric permittivity of the substrate. This approach harnesses the efficiency of convolutional neural networks with pattern recognition tasks and extends previous efforts to employ neural networks for the simulation of parameterized circuit geometries. Training is based on full-wave simulation data generated via the Finite-Difference Time-Domain (FDTD) method over a target frequency range. To accelerate the generation of such data, we build a hybrid neural network including recursive neural network modules, to compensate numerical dispersion errors in coarse-grid FDTD. This novel dispersion compensation scheme allows us to generate accurate FDTD training data from fast, coarse-grid simulations.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on Their Layouts

Sarris

2022

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

show abstract

Design Space and Frequency Extrapolation: Using Neural Networks

Bhatti¹,

Ambasana

Swaminathan

2021

IEEE Microwave

View full text Add to dashboard Cite

Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on their Layouts

Qi¹,

Sarris²

2022

Preprint

View full text Add to dashboard Cite

<div> <div> <div> <p>This paper demonstrates a deep learning based methodology for the rapid simulation of planar microwave circuits based on their layouts. We train convolutional neural networks to compute the scattering parameters of general, two- port circuits consisting of a metallization layer printed on a grounded dielectric substrate, by processing the metallization pattern along with the thickness and dielectric permittivity of the substrate. This approach harnesses the efficiency of convolutional neural networks with pattern recognition tasks and extends previous efforts to employ neural networks for the simulation of parameterized circuit geometries. Training is based on full-wave simulation data generated via the Finite-Difference Time-Domain (FDTD) method over a target frequency range. To accelerate the generation of such data, we build a hybrid neural network including recursive neural network modules, to compensate numerical dispersion errors in coarse-grid FDTD. This novel dispersion compensation scheme allows us to generate accurate FDTD training data from fast, coarse-grid simulations. </p> </div> </div> </div>

show abstract

Design Space Extrapolation for Power Delivery Networks using a Transposed Convolutional Net

Cited by 3 publications

References 7 publications

Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on Their Layouts

Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on Their Layouts

Design Space and Frequency Extrapolation: Using Neural Networks

Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on their Layouts

Contact Info

Product

Resources

About