Parametric S-Parameters for PCB based Power Delivery Network Design Using Machine Learning

Author:

Schierholz Morten1,Erdin Ihsan2,Balachandran Jayaprakash3,Yang Cheng1,Schuster Christian1

Affiliation:

1. Hamburg University of Technology (TUHH),Institut für Theoretische Elektrotechnik,Hamburg,Germany

2. Celestica Inc.,Ottawa,ON,Canada

3. Cisco Inc.,San Jose,CA,USA

Publisher

IEEE

Reference15 articles.

1. Physics-Based Via and Trace Models for Efficient Link Simulation on Multilayer Structures Up to 40 GHz

2. Efficient Crosstalk Analysis of Differential Links on Printed Circuit Boards Up to 100 GHz

3. Special Session on Power Integrity Techniques: Contour Integral Method for Rapid Computation of Power/Ground Plane Impedance;duan,2010

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1. Dimensional Reduction by Auto-Encoders in Machine Learning Based Power Integrity Analysis;2024 IEEE 28th Workshop on Signal and Power Integrity (SPI);2024-05-12

2. A 3-D Convolutional Network for Fast and Accurate S/Z-Parameters Prediction of Si-Interposer Power Distribution Network With Through Silicon Vias;IEEE Transactions on Circuits and Systems II: Express Briefs;2024-05

3. Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN;Sensors;2024-03-30

4. Broadband Modeling of Power Distribution Networks;2024 SICE International Symposium on Control Systems (SICE ISCS);2024-03-18

5. Engineering-Informed Design Space Reduction for PCB-Based Power Delivery Networks;IEEE Transactions on Components, Packaging and Manufacturing Technology;2023-10

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