EMI Radiation Prediction and Structure Optimization of Packages by Deep Learning
With a rapid increase in operating frequency and package complexity, conventional analysis methods cannot efficiently cope with current complex electromagnetic interference (EMI) issues. In this paper, a new method built on a deep neural network (DNN) model is proposed to accurately and rapidly pred...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8758619/ |
| _version_ | 1818665189800673280 |
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| author | Hang Jin Hanzhi Ma Mark D. Butala En-Xiao Liu Er-Ping Li |
| author_facet | Hang Jin Hanzhi Ma Mark D. Butala En-Xiao Liu Er-Ping Li |
| author_sort | Hang Jin |
| collection | DOAJ |
| description | With a rapid increase in operating frequency and package complexity, conventional analysis methods cannot efficiently cope with current complex electromagnetic interference (EMI) issues. In this paper, a new method built on a deep neural network (DNN) model is proposed to accurately and rapidly predict the maximum radiated electric field at 3-meters of a wire-bond ball grid array (WB-BGA) package. The key hyper-parameters of the DNN model, such as the learning rate and type of optimizers, are discussed in depth so as to attain optimal performance. Predicted radiation results by the DNN model and the results of the full-wave simulation show good agreement. Once DNN is trained, the prediction time is in the order of milliseconds and the model size is in megabytes, which can acquire the predicted radiation quickly and accurately and save storage space. Furthermore, to prevent the radiation from exceeding requirements, package structures are optimized by adjusting those parameters sensitive to radiation and disregarding insensitive parameters. The sensitivity of these WB-BGA package structural parameters to EMI radiation can be analyzed quickly based on data with different deviations generated by the trained DNN model. The sensitive parameters are adjusted according to their correlation with EMI radiation. The effectiveness and feasibility of the optimization method are verified by the WB-BGA package with two sets of different structural parameters. |
| first_indexed | 2024-12-17T05:44:41Z |
| format | Article |
| id | doaj.art-4fe5c88be2554fa48cf6264b99b070df |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:44:41Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4fe5c88be2554fa48cf6264b99b070df2022-12-21T22:01:21ZengIEEEIEEE Access2169-35362019-01-017937729378010.1109/ACCESS.2019.29271608758619EMI Radiation Prediction and Structure Optimization of Packages by Deep LearningHang Jin0https://orcid.org/0000-0002-9744-1982Hanzhi Ma1Mark D. Butala2En-Xiao Liu3Er-Ping Li4College of Information Science and Electronic Engineering, Zhejiang University–University of Illinois at Urbana–Champaign Institute, Zhejiang University, Hangzhou, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University–University of Illinois at Urbana–Champaign Institute, Zhejiang University, Hangzhou, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University–University of Illinois at Urbana–Champaign Institute, Zhejiang University, Hangzhou, ChinaAgency for Science Technology and Research, Institute of High Performance Computing, SingaporeCollege of Information Science and Electronic Engineering, Zhejiang University–University of Illinois at Urbana–Champaign Institute, Zhejiang University, Hangzhou, ChinaWith a rapid increase in operating frequency and package complexity, conventional analysis methods cannot efficiently cope with current complex electromagnetic interference (EMI) issues. In this paper, a new method built on a deep neural network (DNN) model is proposed to accurately and rapidly predict the maximum radiated electric field at 3-meters of a wire-bond ball grid array (WB-BGA) package. The key hyper-parameters of the DNN model, such as the learning rate and type of optimizers, are discussed in depth so as to attain optimal performance. Predicted radiation results by the DNN model and the results of the full-wave simulation show good agreement. Once DNN is trained, the prediction time is in the order of milliseconds and the model size is in megabytes, which can acquire the predicted radiation quickly and accurately and save storage space. Furthermore, to prevent the radiation from exceeding requirements, package structures are optimized by adjusting those parameters sensitive to radiation and disregarding insensitive parameters. The sensitivity of these WB-BGA package structural parameters to EMI radiation can be analyzed quickly based on data with different deviations generated by the trained DNN model. The sensitive parameters are adjusted according to their correlation with EMI radiation. The effectiveness and feasibility of the optimization method are verified by the WB-BGA package with two sets of different structural parameters.https://ieeexplore.ieee.org/document/8758619/Deep learning algorithmdeep neural networkelectromagnetic interferenceradiation predictionsensitivity analysisstructure optimization |
| spellingShingle | Hang Jin Hanzhi Ma Mark D. Butala En-Xiao Liu Er-Ping Li EMI Radiation Prediction and Structure Optimization of Packages by Deep Learning IEEE Access Deep learning algorithm deep neural network electromagnetic interference radiation prediction sensitivity analysis structure optimization |
| title | EMI Radiation Prediction and Structure Optimization of Packages by Deep Learning |
| title_full | EMI Radiation Prediction and Structure Optimization of Packages by Deep Learning |
| title_fullStr | EMI Radiation Prediction and Structure Optimization of Packages by Deep Learning |
| title_full_unstemmed | EMI Radiation Prediction and Structure Optimization of Packages by Deep Learning |
| title_short | EMI Radiation Prediction and Structure Optimization of Packages by Deep Learning |
| title_sort | emi radiation prediction and structure optimization of packages by deep learning |
| topic | Deep learning algorithm deep neural network electromagnetic interference radiation prediction sensitivity analysis structure optimization |
| url | https://ieeexplore.ieee.org/document/8758619/ |
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