Pub Date : 2024-07-18DOI: 10.1109/TNS.2024.3426196
{"title":"IEEE Transactions on Nuclear Science information for authors","authors":"","doi":"10.1109/TNS.2024.3426196","DOIUrl":"https://doi.org/10.1109/TNS.2024.3426196","url":null,"abstract":"","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10604715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-16DOI: 10.1109/TNS.2024.3429172
Lei Wu;Shangli Dong;Xiaodong Xu;Yadong Wei;Zhongli Liu;Weiqi Li;Jianqun Yang;Xingji Li
The accumulated radiation effects of preirradiation from different radiation sources on single-event burnout (SEB) of silicon carbide metal-oxide-semiconductor field-effect transistors (SiC MOSFETs) were investigated. The displacement damage (DD) was introduced by preirradiation of silicon ions, and compared with the devices without preirradiation, it was found that it is had for SEB to occur in the devices with DD introduced after silicon ion irradiations. In contrast, for gamma ray preirradiation, it was found that SEB occurs more easily in the radiated devices by gamma ray. In addition, technology computer aided design (TCAD) is used to simulate the SEB of the devices, and the bulk defect increases the recombination rate of the devices and leads to the decrease of the current density. At the same voltage, the smaller the current density is, the lower the thermal effect will be, and SEB hardly occurs. The drain current and lattice temperature of the devices with oxide charges are higher, and SEB occurs more easily. The simulation results are reasonably consistent with the experimental results. This study provides a valuable reference for the method of SEB hardening.
{"title":"Influence of Accumulated Radiation Effects on Single-Event Burnout in SiC MOSFETs","authors":"Lei Wu;Shangli Dong;Xiaodong Xu;Yadong Wei;Zhongli Liu;Weiqi Li;Jianqun Yang;Xingji Li","doi":"10.1109/TNS.2024.3429172","DOIUrl":"10.1109/TNS.2024.3429172","url":null,"abstract":"The accumulated radiation effects of preirradiation from different radiation sources on single-event burnout (SEB) of silicon carbide metal-oxide-semiconductor field-effect transistors (SiC MOSFETs) were investigated. The displacement damage (DD) was introduced by preirradiation of silicon ions, and compared with the devices without preirradiation, it was found that it is had for SEB to occur in the devices with DD introduced after silicon ion irradiations. In contrast, for gamma ray preirradiation, it was found that SEB occurs more easily in the radiated devices by gamma ray. In addition, technology computer aided design (TCAD) is used to simulate the SEB of the devices, and the bulk defect increases the recombination rate of the devices and leads to the decrease of the current density. At the same voltage, the smaller the current density is, the lower the thermal effect will be, and SEB hardly occurs. The drain current and lattice temperature of the devices with oxide charges are higher, and SEB occurs more easily. The simulation results are reasonably consistent with the experimental results. This study provides a valuable reference for the method of SEB hardening.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-15DOI: 10.1109/TNS.2024.3423695
Hongfang Zhang;Adam Stavola;Hal Ferguson;Bence Budavari;Chiman Kwan;Hongyi Wu;Jiang Li
Controlling the dose of radiation exposure in potential radioactive facilities is critical for ensuring the safety of staff and the public. In this article, we developed machine learning (ML) models to estimate radiation exposure efficiently at the Thomas Jefferson National Accelerator Facility (JLab), aiming to enhance safety in both accelerator facilities and public areas. Multiple sensors were deployed around the three experimental halls at JLab. Data on single-beam currents, energy levels, and radiation values at the sensor locations were collected during accelerator operation. We proposed a multitask learning (MTL) model for radiation estimation, using either 1-D convolutional neural networks (1D CNNs) or long short-term memory (LSTM) networks as the backbone. The proposed model was trained to simultaneously estimate radiation levels at the sensor locations. Experimental results demonstrated that the proposed model with LSTM backbone achieved the best estimation performance, with an average $R {^{{2}}}$