TY - GEN
T1 - Inverse Design of Power Delivery Networks using Invertible Neural Networks
AU - Bhatti, Osama Waqar
AU - Ambasana, Nikita
AU - Swaminathan, Madhavan
N1 - 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.
UR - https://www.scopus.com/pages/publications/85123207981
UR - https://www.scopus.com/pages/publications/85123207981#tab=citedBy
U2 - 10.1109/EPEPS51341.2021.9609211
DO - 10.1109/EPEPS51341.2021.9609211
M3 - Conference contribution
AN - SCOPUS:85123207981
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 -