A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings
A deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dummy metal fillings (DMFs) is proposed. By comparing different activation functions and loss functions, a deep neural network for DMF modeling is built using a smooth maximum unit activation function and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/24/4214 |
| _version_ | 1797460003014049792 |
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| author | Xiangliang Li Yijie Tang Peng Zhao Shichang Chen Kuiwen Xu Gaofeng Wang |
| author_facet | Xiangliang Li Yijie Tang Peng Zhao Shichang Chen Kuiwen Xu Gaofeng Wang |
| author_sort | Xiangliang Li |
| collection | DOAJ |
| description | A deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dummy metal fillings (DMFs) is proposed. By comparing different activation functions and loss functions, a deep neural network for DMF modeling is built using a smooth maximum unit activation function and log-cosh loss function. The parasitic capacitive effect of DMFs is quickly and accurately extracted though this model, and the effective permittivity can be obtained subsequently. An on-chip inductor containing DMFs with different filling densities is analyzed using this proposed method and compared with the electromagnetic simulation of entire structures. The results validate the accuracy and efficiency of this proposed method. |
| first_indexed | 2024-03-09T16:58:59Z |
| format | Article |
| id | doaj.art-d0d78e32dd5d4543a4d4684de900d23f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T16:58:59Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d0d78e32dd5d4543a4d4684de900d23f2023-11-24T14:32:20ZengMDPI AGElectronics2079-92922022-12-011124421410.3390/electronics11244214A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal FillingsXiangliang Li0Yijie Tang1Peng Zhao2Shichang Chen3Kuiwen Xu4Gaofeng Wang5Engineering Research Center of Smart Microsensors and Microsystems, Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaEngineering Research Center of Smart Microsensors and Microsystems, Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaEngineering Research Center of Smart Microsensors and Microsystems, Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaEngineering Research Center of Smart Microsensors and Microsystems, Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaEngineering Research Center of Smart Microsensors and Microsystems, Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaEngineering Research Center of Smart Microsensors and Microsystems, Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaA deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dummy metal fillings (DMFs) is proposed. By comparing different activation functions and loss functions, a deep neural network for DMF modeling is built using a smooth maximum unit activation function and log-cosh loss function. The parasitic capacitive effect of DMFs is quickly and accurately extracted though this model, and the effective permittivity can be obtained subsequently. An on-chip inductor containing DMFs with different filling densities is analyzed using this proposed method and compared with the electromagnetic simulation of entire structures. The results validate the accuracy and efficiency of this proposed method.https://www.mdpi.com/2079-9292/11/24/4214activation functioncapacitancedummy metal fills (DMFs)deep learning |
| spellingShingle | Xiangliang Li Yijie Tang Peng Zhao Shichang Chen Kuiwen Xu Gaofeng Wang A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings Electronics activation function capacitance dummy metal fills (DMFs) deep learning |
| title | A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings |
| title_full | A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings |
| title_fullStr | A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings |
| title_full_unstemmed | A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings |
| title_short | A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings |
| title_sort | deep learning approach for efficient electromagnetic analysis of on chip inductor with dummy metal fillings |
| topic | activation function capacitance dummy metal fills (DMFs) deep learning |
| url | https://www.mdpi.com/2079-9292/11/24/4214 |
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