Lai Jung-Pin, Lin Ying-Lei, Lin Ho-Chuan, Shih Chih-Yuan, Wang Yu-Po, Pai Ping-Feng
PhD Program in Strategy and Development of Emerging Industries, National Chi Nan University, Puli Nantou 54561, Taiwan.
Siliconware Precision Industries Co., Ltd. No. 123, Sec. 3, Dafeng Rd., Dafeng Vil., Tanzi Dist., Taichung City 42749, Taiwan.
Micromachines (Basel). 2023 Jan 20;14(2):265. doi: 10.3390/mi14020265.
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 (), root mean square error (), 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.
印刷电路板（PCB）的传输特性确保了信号完整性并支持整个电路系统，其中阻抗匹配在高速PCB电路设计中至关重要。由于影响阻抗的因素与PCB生产工艺密切相关，电路设计师和制造商必须共同努力调整目标阻抗以维持信号完整性。使用了包括决策树（DT）、随机森林（RF）、极端梯度提升（XGBoost）、分类提升（CatBoost）和轻量级梯度提升机（LightGBM）在内的五种机器学习模型来预测目标阻抗值。此外，使用Optuna算法来确定预测模型的超参数。本研究应用基于树的机器学习技术与Optuna来预测阻抗。结果表明，结合Optuna的五种基于树的机器学习模型在平均绝对百分比误差（）、均方根误差（）和决定系数（R2）这三项测量指标上能够产生令人满意的预测精度。同时，结合Optuna的LightGBM模型表现优于其他模型。此外，通过使用Optuna调整机器学习模型的参数，可以提高阻抗匹配的精度。因此，本研究结果表明，结合Optuna的基于树的机器学习技术是用于预测电路分析阻抗值的一种可行且有前景的替代方法。
