Inverse Design of Power Delivery Networks using Invertible Neural Networks

Osama Waqar Bhatti; Nikita Ambasana; Madhavan Swaminathan

doi:10.1109/EPEPS51341.2021.9609211

Inverse Design of Power Delivery Networks using Invertible Neural Networks

Osama Waqar Bhatti, Nikita Ambasana, Madhavan Swaminathan

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

In this paper, we achieve an inverse mapping of a power delivery network's physical and geometrical properties to the impedance specification over a wide range of frequency through invertible neural networks. Training the machine learning network involves learning over a variety of stackup specifications. Once the invertible network is trained, the user can specify target impedance spec and obtain the probability density of the values of the design space that most likely satisfies the design specifications.

Original language	English (US)
Title of host publication	EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665442695
DOIs	https://doi.org/10.1109/EPEPS51341.2021.9609211
State	Published - 2021
Event	30th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2021 - Austin, United States Duration: Oct 17 2021 → Oct 20 2021

Publication series

Name	EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference	30th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2021
Country/Territory	United States
City	Austin
Period	10/17/21 → 10/20/21

All Science Journal Classification (ASJC) codes

Safety, Risk, Reliability and Quality
Electronic, Optical and Magnetic Materials
Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/EPEPS51341.2021.9609211

Cite this

Bhatti, O. W., Ambasana, N., & Swaminathan, M. (2021). Inverse Design of Power Delivery Networks using Invertible Neural Networks. In EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EPEPS51341.2021.9609211

Bhatti, Osama Waqar ; Ambasana, Nikita ; Swaminathan, Madhavan. / Inverse Design of Power Delivery Networks using Invertible Neural Networks. EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc., 2021. (EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems).

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title = "Inverse Design of Power Delivery Networks using Invertible Neural Networks",

abstract = "In this paper, we achieve an inverse mapping of a power delivery network's physical and geometrical properties to the impedance specification over a wide range of frequency through invertible neural networks. Training the machine learning network involves learning over a variety of stackup specifications. Once the invertible network is trained, the user can specify target impedance spec and obtain the probability density of the values of the design space that most likely satisfies the design specifications.",

author = "Bhatti, {Osama Waqar} and Nikita Ambasana and Madhavan Swaminathan",

note = "Funding Information: ACKNOWLEDGMENT This material is based upon work supported by the National Science Foundation under Grant No. CNS 16-24810 - Center for Advanced Electronics through Machine Learning (CAEML). Publisher Copyright: {\textcopyright} 2021 IEEE.; 30th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2021 ; Conference date: 17-10-2021 Through 20-10-2021",

year = "2021",

doi = "10.1109/EPEPS51341.2021.9609211",

language = "English (US)",

series = "EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems",

address = "United States",

}

Bhatti, OW, Ambasana, N & Swaminathan, M 2021, Inverse Design of Power Delivery Networks using Invertible Neural Networks. in EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems. EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems, Institute of Electrical and Electronics Engineers Inc., 30th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2021, Austin, United States, 10/17/21. https://doi.org/10.1109/EPEPS51341.2021.9609211

Inverse Design of Power Delivery Networks using Invertible Neural Networks. / Bhatti, Osama Waqar; Ambasana, Nikita; Swaminathan, Madhavan.
EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc., 2021. (EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Inverse Design of Power Delivery Networks using Invertible Neural Networks

AU - Bhatti, Osama Waqar

AU - Ambasana, Nikita

AU - Swaminathan, Madhavan

N1 - Funding Information: ACKNOWLEDGMENT This material is based upon work supported by the National Science Foundation under Grant No. CNS 16-24810 - Center for Advanced Electronics through Machine Learning (CAEML). Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - In this paper, we achieve an inverse mapping of a power delivery network's physical and geometrical properties to the impedance specification over a wide range of frequency through invertible neural networks. Training the machine learning network involves learning over a variety of stackup specifications. Once the invertible network is trained, the user can specify target impedance spec and obtain the probability density of the values of the design space that most likely satisfies the design specifications.

AB - In this paper, we achieve an inverse mapping of a power delivery network's physical and geometrical properties to the impedance specification over a wide range of frequency through invertible neural networks. Training the machine learning network involves learning over a variety of stackup specifications. Once the invertible network is trained, the user can specify target impedance spec and obtain the probability density of the values of the design space that most likely satisfies the design specifications.

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M3 - Conference contribution

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T3 - EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems

BT - EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 30th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2021

Y2 - 17 October 2021 through 20 October 2021

ER -

Bhatti OW, Ambasana N, Swaminathan M. Inverse Design of Power Delivery Networks using Invertible Neural Networks. In EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc. 2021. (EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems). doi: 10.1109/EPEPS51341.2021.9609211

Inverse Design of Power Delivery Networks using Invertible Neural Networks

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