Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
In this paper, a method based on deep learning is proposed to predict the parameters of bonded metal wires, which solves the problem that the transmission characteristics of S-parameters cannot be predicted. In an X-band microwave chip circuit, gold wire bonding technology is often used to realize b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9631 |
| _version_ | 1827728325606375424 |
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| author | Shenglin Yu Hao Li |
| author_facet | Shenglin Yu Hao Li |
| author_sort | Shenglin Yu |
| collection | DOAJ |
| description | In this paper, a method based on deep learning is proposed to predict the parameters of bonded metal wires, which solves the problem that the transmission characteristics of S-parameters cannot be predicted. In an X-band microwave chip circuit, gold wire bonding technology is often used to realize bonding interconnection, and the arch height and span of the bonded metal wire will have a great influence on the microwave transmission characteristics. By predicting the S-parameters of the bonded metal wire, the relationship between the structure parameters of the single wire and the transmission performance of the microwave device can be deduced. First, the gold wire bonding model is established in HFSS simulation software. After parameter optimization, the simulation results meet the requirements of establishing data sets. Then the sampling range of S parameters is set, and the parameters are scanned to establish data sets. Second, the artificial neural network model is built. The model adds a dropout mechanism to the hidden layer to enhance the generalization of the neural network, prevent overfitting phenomenon, and significantly improve the model’s prediction performance. Finally, the model predicts the corresponding relationship between the arch height and span of the bonding wire and the insertion loss, return loss and standing wave ratio. The mean square error of the test set is less than 0.8. The experimental results show that compared with the traditional process measurement method, this method can quickly and accurately infer whether the microwave characteristics of the bonded product are qualified, which greatly reduces the time and economic cost of the engineer and improves the work efficiency. |
| first_indexed | 2024-03-10T23:27:40Z |
| format | Article |
| id | doaj.art-619e7a58a25b4b5ba02b79bd7c882603 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:27:40Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-619e7a58a25b4b5ba02b79bd7c8826032023-11-19T07:49:42ZengMDPI AGApplied Sciences2076-34172023-08-011317963110.3390/app13179631Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire BondingShenglin Yu0Hao Li1School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaIn this paper, a method based on deep learning is proposed to predict the parameters of bonded metal wires, which solves the problem that the transmission characteristics of S-parameters cannot be predicted. In an X-band microwave chip circuit, gold wire bonding technology is often used to realize bonding interconnection, and the arch height and span of the bonded metal wire will have a great influence on the microwave transmission characteristics. By predicting the S-parameters of the bonded metal wire, the relationship between the structure parameters of the single wire and the transmission performance of the microwave device can be deduced. First, the gold wire bonding model is established in HFSS simulation software. After parameter optimization, the simulation results meet the requirements of establishing data sets. Then the sampling range of S parameters is set, and the parameters are scanned to establish data sets. Second, the artificial neural network model is built. The model adds a dropout mechanism to the hidden layer to enhance the generalization of the neural network, prevent overfitting phenomenon, and significantly improve the model’s prediction performance. Finally, the model predicts the corresponding relationship between the arch height and span of the bonding wire and the insertion loss, return loss and standing wave ratio. The mean square error of the test set is less than 0.8. The experimental results show that compared with the traditional process measurement method, this method can quickly and accurately infer whether the microwave characteristics of the bonded product are qualified, which greatly reduces the time and economic cost of the engineer and improves the work efficiency.https://www.mdpi.com/2076-3417/13/17/9631microwave chipwire bondingartificial neural networkparameter prediction |
| spellingShingle | Shenglin Yu Hao Li Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding Applied Sciences microwave chip wire bonding artificial neural network parameter prediction |
| title | Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding |
| title_full | Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding |
| title_fullStr | Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding |
| title_full_unstemmed | Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding |
| title_short | Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding |
| title_sort | prediction of microwave characteristic parameters based on mmic gold wire bonding |
| topic | microwave chip wire bonding artificial neural network parameter prediction |
| url | https://www.mdpi.com/2076-3417/13/17/9631 |
| work_keys_str_mv | AT shenglinyu predictionofmicrowavecharacteristicparametersbasedonmmicgoldwirebonding AT haoli predictionofmicrowavecharacteristicparametersbasedonmmicgoldwirebonding |