Abstract
A deep neural network (DNN) model is developed in this paper for fast prediction of time-domain reflectometer (TDR) impedance for differential vias in high-speed printed circuit boards (PCBs). Unlike traditional empirical linear modeling approaches, the DNN model more accurately maps the nonlinearity between via geometrical parameters and differential impedance. How to select neural network type, training functions and how to select an efficient set of training data are discussed in the paper. Good correlations between the predicted impedances and target values prove the accuracy and reliability of the DNN model. The calculation time for a single data point is reduced to milliseconds, so that the design efficiency of high-speed differential via design is significantly increased.
| Original language | American English |
|---|---|
| Journal | Proceedings of the 2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (2018, Long Beach, CA) |
| DOIs | |
| State | Published - Jul 1 2018 |
Keywords
- Busbars
- Deep learning method
- Deep neural networks (DNN)
- Design efficiency
- Differential impedance
- Differential via
- Electromagnetic compatibility
- Geometry
- High-speed channel design
- High-speed channels
- Learning methods
- Printed circuit board (PCBs)
- Printed circuit boards
- Reflectometers
- TDR impedance
- Time domain analysis
- Time-domain reflectometers
- Via model
- Via modeling
Disciplines
- Electrical and Computer Engineering
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