Flexible Inverse Design of Microwave Filter Customized on Demand With Wavelet Transform Deep Learning

被引：1

作者：

Xu, Kuiwen ^{[1
]}

Wang, Jianguo ^{[1
]}

Cai, Jialin ^{[1
]}

Ma, Xuetiao ^{[1
]}

Lv, Qinyi ^{[2
]}

Chen, Shichang ^{[1
]}

Liu, Jie ^{[3
,4
]}

Liu, Jun ^{[1
]}

机构：

[1] Hangzhou Dianzi University, Innovation Center for Electronic Design Automation Technology, The Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou

[2] Chang'an Campus, Northwestern Polytechnical University, School of Electronic Information, Xi'an

[3] University of Electronic Science and Technology of China, School of Electronic Science and Engineering, Chengdu

[4] Information Science Academy China Electronics Technology Group Corporation, Beijing

来源：

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems | 2025年 / 44卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Electromagnetics (EMs) inverse modeling; high quality sampling (HQS); machine learning; microwave device; wavelet transform;

D O I：

10.1109/TCAD.2024.3451329

中图分类号：

学科分类号：

摘要：

Artificial intelligence (AI) techniques are increasingly being used for the inverse design of microwave devices. However, several challenges, including intensive computation costs for training samples, high-dimensional data, nonuniformity, and low-quality samples in the design space, can negatively impact the final modeling performance. To alleviate these issues, a high-quality sampling inverse design scheme incorporating wavelet transform deep learning (HQS-WTDL) is proposed to achieve customized, automated microwave filter design. In the forward simulation-based sampling, particle swarm optimization (PSO) is used to tentatively select rule-defined samples. Multilabel synthetic minority over-sampling technique (MLSMOTE) is then applied to enlarge the training samples and improve their uniformity in the design space. An inverse modeling approach using neural networks to map the nonlinear relationship between a given set of S-parameters and required filter structural parameters is presented. Given that the dimension of the S-parameters is much higher than that of the structure parameters, the corresponding neural network used in this approach is deep and complex, with multiple layers and neurons. To reduce the number of input variables, the S-parameters are subjected to wavelet transformation, allowing for more efficient representation by the neural network. The proposed method is validated using a band-pass microstrip hairpin filter as an example. Results demonstrate that the proposed approach achieves better modeling effectiveness and inverse design efficiency than conventional methods. In addition, the proposed method allows for fast customization of device parameters, such as center frequencies and bandwidths with good prediction accuracy. © 1982-2012 IEEE.

引用

页码：806 / 817

页数：11

共 34 条

[1] Guillena E., Li W., Montoro G., Quaglia R., Gilabert P.L., Reconfigurable DPD based on ANNs for wideband load modulated balanced amplifiers under dynamic operation from 1.8 to 2.4 GHz, IEEE Trans. Microw. Theory Tech., 70, 1, pp. 453-465, (2022)
[2] Lee D., Shin G., Lee S., Kim K., Oh T.H., Song H.J., Neural-network-based automated synthesis of transformer matching circuits for RF amplifier design, IEEE Trans. Microw. Theory Tech., 70, 11, pp. 4726-4739, (2022)
[3] Huang J., Tao C., Yang F., Yan C., Zhou D., Zeng X., Bayesian optimization approach for RF circuit synthesis via multitask neural network enhanced gaussian process, IEEE Trans. Microw. Theory Tech., 70, 11, pp. 4787-4795, (2022)
[4] Hakhamaneshi K., Nassar M., Phielipp M., Abbeel P., Stojanovic V., Pretraining graph neural networks for few-shot analog circuit modeling and design, IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., 42, 7, pp. 2163-2173, (2023)
[5] Qi S., Sarris C.D., Deep neural networks for rapid simulation of planar microwave circuits based on their layouts, IEEE Trans. Microw. Theory Tech., 70, 11, pp. 4805-4815, (2022)
[6] Javaid A., Achar R., Tripathi J.N., Development of knowledge-based artificial neural networks for analysis of PSIJ in CMOS inverter circuits, IEEE Trans. Microw. Theory Tech., 71, 4, pp. 1428-1438, (2023)
[7] Ballard Z., Brown C., Madni A.M., Ozcan A., Machine learning and computation-enabled intelligent sensor design, Nature Mach. Intell., 3, 7, pp. 556-565, (2021)
[8] Jafarieh A., Nouri M., Noorollahi H., Behroozi H., Mallat N.K., An optimized wearable textile antenna using surrogate ensemble learning for ISM on-body communications, IEEE Trans. Compon. Packag. Manuf. Technol., 13, 8, pp. 1262-1270, (2023)
[9] Ahmed H., Xiaoping Z., Bello H., Inverse design of multiparameter antenna using hybrid machine learning-driven training dataset, Microw. Opt. Technol. Lett.., 66, 1
[10] Sarker N., Podder P., Mondal M.R.H., Shafin S.S., Kamruzzaman J., Applications of machine learning and deep learning in antenna design, optimization, and selection: A review, IEEE Access, 11, pp. 103890-103915, (2023)

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