Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning
Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations....
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/14/3/502 |
| _version_ | 1797610101354266624 |
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| author | Rong Zhao Shulong Wang Shougang Du Jinbin Pan Lan Ma Shupeng Chen Hongxia Liu Yilei Chen |
| author_facet | Rong Zhao Shulong Wang Shougang Du Jinbin Pan Lan Ma Shupeng Chen Hongxia Liu Yilei Chen |
| author_sort | Rong Zhao |
| collection | DOAJ |
| description | Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 10<sup>2</sup> and 1.38 × 10<sup>3</sup> times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices. |
| first_indexed | 2024-03-11T06:10:49Z |
| format | Article |
| id | doaj.art-e5f6b393bc1b45ef9303a45c8730e3a6 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-11T06:10:49Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-e5f6b393bc1b45ef9303a45c8730e3a62023-11-17T12:41:45ZengMDPI AGMicromachines2072-666X2023-02-0114350210.3390/mi14030502Prediction of Single-Event Effects in FDSOI Devices Based on Deep LearningRong Zhao0Shulong Wang1Shougang Du2Jinbin Pan3Lan Ma4Shupeng Chen5Hongxia Liu6Yilei Chen7School of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaSingle-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 10<sup>2</sup> and 1.38 × 10<sup>3</sup> times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.https://www.mdpi.com/2072-666X/14/3/502deep neural network (DNN)FDSOI devicessingle-event effect (SEE)drain transient current pulse |
| spellingShingle | Rong Zhao Shulong Wang Shougang Du Jinbin Pan Lan Ma Shupeng Chen Hongxia Liu Yilei Chen Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning Micromachines deep neural network (DNN) FDSOI devices single-event effect (SEE) drain transient current pulse |
| title | Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning |
| title_full | Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning |
| title_fullStr | Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning |
| title_full_unstemmed | Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning |
| title_short | Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning |
| title_sort | prediction of single event effects in fdsoi devices based on deep learning |
| topic | deep neural network (DNN) FDSOI devices single-event effect (SEE) drain transient current pulse |
| url | https://www.mdpi.com/2072-666X/14/3/502 |
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