In this paper, a novel multi-physics parametric modeling approach using artificial neural networks (ANNs) for microwave passive components is proposed. In the proposed approach, the ANN is used to learn the nonlinear relationships between electromagnetic (EM) behaviors and multi-physics design variables. The trained model can accurately represent the EM responses of the passive components with respect to the multi-physics input parameters. Therefore, the proposed model can provide accurate and fast prediction of EM responses using low computational cost and little time for multi-physics design. The advantage of the proposed model is demonstrated by two microwave examples: the proposed model can save about 98% computational cost compared with the EM model, and the CPU time of the proposed model is less than 0.1 s while that of the EM model needs many minutes.
1. Ren, L. and C. Gong, "Modified hybrid model of boost converters for parameter identification of passive components," IET Power Electronics, Vol. 11, 764-771, 2018, http://dx.doi.org/10.1049/iet-pel.2017.0528. doi:10.1049/iet-pel.2017.0528
2. Triverio, P., M. Nakhla, and S. Grivet-Talocia, "Extraction of parametric circuit models from scattering parameters of passive RF components," The 5th European Microwave Integrated Circuits Conference, 393-396, Paris, 2010, https://doi.org/10.23919/EUMC.2010.5616354.
3. Aldemir, T., R. Denning, U. Catalyurek, and S. Unwin, "Methodology development for passive component reliability modeling in a multi-physics simulation environment,", United States: N. p., 2015, https://doi.org/10.2172/1214664.
4. Qian, L. X., S. l. Zheng, and H. J. Li, "Research on the multi-physics simulation and chip implementation of piezoelectric contour mode resonator," Symposium on Piezoelectricity, Acoustic Waves, and Device Applications (SPAWDA), 217-221, Chengdu, 2017, https://doi.org/10.1109/SPAWDA.2017.8340325. doi:10.1109/SPAWDA.2017.8340325
5. Tang, H., D. Yang, and G. Q. Zhang, "Multi-physics modeling of LED-based luminaires under temperature and humidity environment," 13th International Conference on Electronic Packaging Technology & High Density Packaging, 803-807, Guilin, 2012, https://doi.org/10.1109/ICEPTHDP.2012.6474733.
6. Liu, E. X., E. P. Li, W. B. Ewe, and H. M. Lee, "Multi-physics modeling of through-silicon vias with equivalent-circuit approach," 19th Topical Meeting on Electrical Performance of Electronic Packaging and Systems, 33-36, Austin, TX, 2010, https://doi.org/10.1109/EPEPS.2010.5642537. doi:10.1109/EPEPS.2010.5642537
7. Yang, X., Z. Wang, Y. Ren, B. Sun, and C. Qian, "Lifetime prediction based on analytical multi-physics simulation for light-emitting diode (LED) systems," 18th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Micros, 1-8, Dresden, 2017, https://doi.org/10.1109/EuroSimE.2017.7926233.
8. Liu, X., Q. Wu, and X. Shi, "Multi-physics analysis of waveguide filters for wireless communication systems," IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 1-2, Beijing, 2016, https://doi.org/10.1109/NEMO.2016.7561628.
9. Yi, X., Y. Wang, M. M. Tentzeris, and R. T. Leon, "Multi-physics modeling and simulation of a slotted patch antenna for wireless strain sensing," Structural Health Monitoring 2013: A Roadmap to Intelligent Structures — Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM, Vol. 2, 1857-1864, 2013, https://doi.org/10.1117/12.2009233.
10. Wang, S., et al., "Abnormal breast detection in mammogram images by feedforward neural network trained by jaya algorithm," Fundamenta Informaticae, Vol. 151, No. 1–4, 191-211, 2017, http://dx.doi.org/10.3233/FI-2017-1487. doi:10.3233/FI-2017-1487
11. Alique, A., et al., "A Neural network-based model for the prediction of cutting force in milling process. A progress study on a real case," IEEE International Symposium on Intelligent Control — Proceedings, Vol. 2000, 121-125, 2000, https://doi.org/10.1109/ISIC.2000.882910.
12. Fe, I. L., et al., "Automatic selection of optimal parameters based on simple soft computing methods. A case study on micro-milling processes," IEEE Transactions on Industrial Informatics, 1-1, 2018, https://doi.org/10.1109/TII.2018.2816971.
13. Kabir, H., L. Zhang, M. Yu, P. H. Aaen, J. Wood, and Q. J. Zhang, "Smart modeling of microwave devices," IEEE Microwave Magazine, Vol. 11, 105-118, 2010, https://doi.org/10.1109/MMM.2010.936079. doi:10.1109/MMM.2010.936079
14. Li, X., J. Gao, and Q. J. Zhang, "Microwave noise modeling for PHEMT using artificial neural network technique," International Journal of RF and Microwave Computer-Aided Engineering, Vol. 19, 187-196, 2009, https://doi.org/10.1002/mmce.v19:2. doi:10.1002/mmce.20339
15. Schmidt, S. R. and R. G. Launsby, Understanding Industrial Designed Experiments, Colorado Springs, Air Force Academy, CO, USA, 1992.
16. Zhang, Q. J. and K. C. Gupta, Neural Networks for RF and Microwave Design, Artech House, Boston, 2000.
17. Na, W., et al., "A unified automated parametric modeling algorithm using knowledge-based neural network and l1 optimization," IEEE Transactions on Microwave Theory & Techniques, Vol. 99, 1-17, 2017, https://doi.org/10.1109/TMTT.2016.2630059.
18. Liu, X., L. P. B. Katehi, W. J. Chappell, and D. Peroulis, "Power handling of electrostatic MEMS evanescent-mode (EVA) tunable bandpass filters," IEEE Transactions on Microwave Theory and Techniques, Vol. 60, 270-283, 2012, https://doi.org/10.1109/TMTT.2011.2176136. doi:10.1109/TMTT.2011.2176136
19. Morro, J. V., P. Soto, H. Esteban, V. E. Boria, C. Bachiller, M. Taroncher, S. Cogollos, and B. Gimeno, "Fast automated design of waveguide filters using aggressive space mapping with a new segmentation strategy and a hybrid optimization algorithm," IEEE Transactions on Microwave Theory and Techniques, Vol. 53, 1130-1142, 2005, https://doi.org/10.1109/TMTT.2005.845685.