{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:01:44Z","timestamp":1757617304357,"version":"3.44.0"},"reference-count":34,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Electron."],"published-print":{"date-parts":[[2025,9,1]]},"DOI":"10.1587\/transele.2024ecp5057","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T17:12:51Z","timestamp":1739812371000},"page":"427-434","source":"Crossref","is-referenced-by-count":0,"title":["Topology Optimization of Microstrip Lines Using Twin Deep Neural Networks for Performance Prediction and Accuracy Evaluation"],"prefix":"10.1587","volume":"E108.C","author":[{"given":"Takuto","family":"JIBIKI","sequence":"first","affiliation":[{"name":"Hokkaido University"},{"name":"Sumitomo Electric Industries, LTD."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takeshi","family":"KAWASAKI","sequence":"additional","affiliation":[{"name":"Sumitomo Electric Industries, LTD."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"TANOMURA","sequence":"additional","affiliation":[{"name":"Sumitomo Electric Industries, LTD."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hajime","family":"IGARASHI","sequence":"additional","affiliation":[{"name":"Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[2] N. Aage, N.A. Mortensen, and O. Sigmund, \u201cTopology optimization of metallic devices for microwave applications,\u201d Int. J. Num. Methods Eng., vol.83, no.2, pp.228-248, 2010. 10.1002\/nme.2837","DOI":"10.1002\/nme.2837"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[3] G. Kiziltas, D. Psychoudakis, J.L. Volakis, and N. Kikuchi, \u201cTopology design optimization of dielectric substrates for bandwidth improvement of a patch antenna,\u201d IEEE Trans. Antennas and Prop., vol.51, no.10, pp.2732-2743, 2003. 10.1109\/tap.2003.817539","DOI":"10.1109\/TAP.2003.817539"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[4] A. Erentok and O. Sigmund, \u201cTopology optimization of sub-wavelength antennas,\u201d IEEE Trans. Antennas and Prop., vol.59, no.1, pp.58-69, 2010. 10.1109\/tap.2010.2090451","DOI":"10.1109\/TAP.2010.2090451"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[5] E. Hassan, E. Wadbro, and M. Berggren, \u201cTopology optimization of metallic antennas,\u201d IEEE Trans. Antennas Propag., vol.62, no.5, pp.2488-2500, 2014. 10.1109\/tap.2014.2309112","DOI":"10.1109\/TAP.2014.2309112"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[6] A. Iguchi, Y. Tsuji, T. Yasui, and K. Hirayama, \u201cTopology optimization of optical waveguide devices based on beam propagation method with sensitivity analysis,\u201d Journal of Lightwave Technology, vol.34, no.18, pp.4214-4220, 2016. 10.1109\/jlt.2016.2597308","DOI":"10.1109\/JLT.2016.2597308"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[7] S. Zhou, W. Li, and Q. Li, \u201cLevel-set based topology optimization for electromagnetic dipole antenna design,\u201d J. Comp. Phys., vol.229, no.19, pp.6915-6930, 2010. 10.1016\/j.jcp.2010.05.030","DOI":"10.1016\/j.jcp.2010.05.030"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[8] S. Yamasaki, T. Nomura, K. Sato, N. Michishita, Y. Yamada, and A. Kawamoto, \u201cLevel set-based topology optimization targeting dielectric resonator-based composite right-and left-handed transmission lines,\u201d International journal for numerical methods in engineering, vol.89, no.10, pp.1272-1295, 2012. 10.1002\/nme.3287","DOI":"10.1002\/nme.3287"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[9] Z. Zhang, Y. Tsuji, T. Yasui, and K. Hirayama, \u201cDesign of ultra-compact triplexer with function-expansion based topology optimization,\u201d Optics express, vol.23, no.4, pp.3937-3950, 2015. 10.1364\/oe.23.003937","DOI":"10.1364\/OE.23.003937"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[10] M. Tomiyasu, K. Morimoto, A. Iguchi, and Y. Tsuji, \u201cA study on function-expansion-based topology optimization without gray area for optimal design of photonic devices,\u201d IEICE Trans. Electron., vol.E103-C, no.11, pp.560-566, 2020. 10.1587\/transele.2019esp0005","DOI":"10.1587\/transele.2019ESP0005"},{"key":"10","unstructured":"[11] N. Hansen, \u201cThe CMA evolution strategy: A tutorial,\u201d arXiv: 1604.00772, 2016."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[12] T. Sato, K. Watanabe, and H. Igarashi, \u201cMultimaterial topology optimization of electric machines based on normalized gaussian network,\u201d IEEE Trans. Magn., vol.51, no.3, 7202604, pp.1-4, 2015. 10.1109\/tmag.2014.2359972","DOI":"10.1109\/TMAG.2014.2359972"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[13] H. Igarashi, Topology optimization and AI-based design of power electronic and electric devices, Academic press, 2024. 10.1016\/C2021-0-01685-0","DOI":"10.1016\/B978-0-32-399166-7.00012-0"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[14] S. Hiruma, M. Ohtani, S. Soma, Y. Kubota, and H. Igarashi, \u201cNovel hybridization of parameter and topology optimizations: Application to permanent magnet motor,\u201d IEEE Trans. Magn., vol.57, no.7, pp.1-4, 2021. 10.1109\/tmag.2021.3078435","DOI":"10.1109\/TMAG.2021.3078435"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[15] S. Hayashi, M. Yatsurugi, Y. Kubota, S. Soma, and H. Igarashi, \u201cTopology Optimization of a Surface Permanent Magnet Motor With High Torque Density,\u201d IEEE Trans. Magn., vol.60, no.3, pp.1-5, 2023. 10.1109\/tmag.2023.3309538","DOI":"10.1109\/TMAG.2023.3309538"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[16] Y. Otomo and H. Igarashi, \u201cA 3-D Topology Optimization of Magnetic Cores for Wireless Power Transfer Device,\u201d IEEE Trans. Magn., vol.55, no.6, 8103005, pp.1-5, 2019. 10.1109\/tmag.2019.2900744","DOI":"10.1109\/TMAG.2019.2900744"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[17] T. Mori, R. Murakami, Y. Sato, F. Campelo, and H. Igarashi, \u201cShape optimization of wideband antennas for microwave energy harvesters using FDTD,\u201d IEEE Trans. Magn., vol.51, no.3, pp.1-4, 2015 10.1109\/tmag.2014.2359677","DOI":"10.1109\/TMAG.2014.2359677"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[18] K. Itoh, H. Nakajima, H. Matsuda, M. Tanaka, and H. Igarashi, \u201cDevelopment of small dielectric lens for slot antenna using topology optimization with normalized Gaussian network,\u201d IEICE Trans. Electronics, vol.E101-C, no.10, pp.784-790, 2018. 10.1587\/transele.e101.c.784","DOI":"10.1587\/transele.E101.C.784"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[19] T. Jibiki, T. Kawasaki, M. Tanomura, and H. Igarashi, \u201cTopology Optimization of Microstrip Lines with Experimental Validation,\u201d IEEE Trans. Magn., vol.60, no.3, 7200504, pp.1-4, 2024. 10.1109\/tmag.2023.3312494","DOI":"10.1109\/TMAG.2023.3312494"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[20] Y. Otomo and H. Igarashi, \u201cTopology optimization of magnetic cores for WPT using the geometry projection method,\u201d COMPEL-The international journal for computation and mathematics in electrical and electronic engineering, vol.41, no.3, pp.889-899, 2022. 10.1108\/compel-02-2021-0064","DOI":"10.1108\/COMPEL-02-2021-0064"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[21] T. Jibiki, T. Kawasaki, M. Tanomura, and H. Igarashi, \u201cTopology Optimization of Microwave Devices with Thin Structure,\u201d IEEE Trans. Magn., vol.60, no.12, pp.1-4, 2024. 10.1109\/tmag.2024.3440468","DOI":"10.1109\/TMAG.2024.3440468"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[22] J.A. Norato, B.K. Bell, and D.A. Tortorelli, \u201cA Geometry Projection Method for Continuum-Based Topology Optimization with Discreate Elements,\u201d Comput. Methods Appl. Mech. Engrg., vol.293, pp.306-327, 2015. 10.1016\/j.cma.2015.05.005","DOI":"10.1016\/j.cma.2015.05.005"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[23] T. Fujisawa and K. Saitoh, \u201cBayesian direct-binary-search algorithm for the efficient design of mosaic-based power splitters,\u201d OSA Continuum, vol.4, no.4, pp.1258-1270, 2021. 10.1364\/osac.422116","DOI":"10.1364\/OSAC.422116"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[24] H. Sasaki and H. Igarashi, \u201cTopology optimization accelerated by deep learning,\u201d IEEE Trans. Magn., vol.55, no.6, pp.1-5, 2019. 10.1109\/tmag.2019.2901906","DOI":"10.1109\/TMAG.2019.2901906"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[25] H. Sasaki, Y. Hidaka, and H. Igarashi, \u201cPrediction of IPM machine torque characteristics using deep learning based on magnetic field distribution,\u201d IEEE Access, vol.10, pp.60814-60822, 2022. 10.1109\/access.2022.3179835","DOI":"10.1109\/ACCESS.2022.3179835"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[26] S. Doi, H. Sasaki, and H. Igarashi, \u201cMulti-objective topology optimization of rotating machines using deep learning,\u201d IEEE Trans. Magn., vol.55, no.6, pp.1-5, 2019. 10.1109\/tmag.2019.2899934","DOI":"10.1109\/TMAG.2019.2899934"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[27] H. Sasaki, Y. Hidaka, and H. Igarashi, \u201cExplainable deep neural network for design of electric motors,\u201d IEEE Trans. Magn., vol.57, no.6, pp.1-4, 2021. 10.1109\/tmag.2021.3063141","DOI":"10.1109\/TMAG.2021.3063141"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[28] H. Sato and H. Igarashi, \u201cFast topology optimization for pm motors using variational autoencoder and neural networks with dropout,\u201d IEEE Trans. Magn., vol.59, no.5, pp.1-4, 2023. 10.1109\/cefc55061.2022.9940771","DOI":"10.1109\/TMAG.2023.3242288"},{"key":"28","unstructured":"[29] T. Jibiki, T. Kawasaki, M. Tanomura, and H. Igarashi, \u201cTopology optimization of a microstrip line with robustness consideration,\u201d Proc. 20th Int. IGTE Symposium, p.51, 2022."},{"key":"29","doi-asserted-by":"publisher","unstructured":"[30] E.A. Karahan, Z. Liu, and K. Sengupta, \u201cDeep-Learning-Based Inverse-Designed Millimeter-Wave Passives and Power Amplifiers,\u201d IEEE J. Solid-State Circuits, vol.58, no.11, pp.3074-3088, 2023. 10.1109\/jssc.2023.3276315","DOI":"10.1109\/JSSC.2023.3276315"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[31] T. Akada and K. Fujimori, \u201cDesigning Microwave Circuits Using Genetic Algorithms Accelerated by Convolutional Neural Networks,\u201d 2020 50th European Microwave Conference (EuMC), Utrecht, Netherlands, pp.61-64, 2021. 10.23919\/eumc48046.2021.9337992","DOI":"10.23919\/EuMC48046.2021.9337992"},{"key":"31","unstructured":"[32] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv, 2014."},{"key":"32","unstructured":"[33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, \u201cAn image is worth 16x16 words: Transformers for image recognition at scale,\u201d International Conference on Learning Representations (ICLR), 2021."},{"key":"33","unstructured":"[34] D.P. Kingma and J. Ba, \u201cAdam: A Method for Stochastic Optimization,\u201d arXiv:1412.6980, 2014."},{"key":"34","unstructured":"[35] J. Kaplan, S. McCandlish, T. Henighan, T. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, \u201cScaling laws for neural language models,\u201d arXiv:2001.08361, 2020."}],"container-title":["IEICE Transactions on Electronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transele\/E108.C\/9\/E108.C_2024ECP5057\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T03:37:26Z","timestamp":1757129846000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transele\/E108.C\/9\/E108.C_2024ECP5057\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":34,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transele.2024ecp5057","relation":{},"ISSN":["0916-8524","1745-1353"],"issn-type":[{"type":"print","value":"0916-8524"},{"type":"electronic","value":"1745-1353"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"article-number":"2024ECP5057"}}