Comparison of Collaborative versus Extended Artificial Neural Networks for PDN Design

Schierholz, Morten; Yang, Cheng Fu; Roy, Kallol; Swaminathan, Madhavan; Schuster, Christian

doi:10.1109/spi48784.2020.9218157

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Schierholz et al [ 13 ] also used an ANN to predict target impedance violations in a large design space. The results of their study revealed that prediction accuracy in the design space for PDN impedance was very satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

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The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

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Section: Introductionmentioning

confidence: 99%