IEEE Transactions on Nuclear Science最新文献_第7页

Radiation Resistant Reinforcement and Integration Technology for 650 V Half Bridge Gate Drive Circuit 650v半桥栅驱动电路的抗辐射加固与集成技术

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-06-08 DOI: 10.1109/TNS.2026.3701049

Ning Tang;Liqiang Ding;Wei Li

{"title":"Radiation Resistant Reinforcement and Integration Technology for 650 V Half Bridge Gate Drive Circuit","authors":"Ning Tang;Liqiang Ding;Wei Li","doi":"10.1109/TNS.2026.3701049","DOIUrl":"https://doi.org/10.1109/TNS.2026.3701049","url":null,"abstract":"Within the space electronics industry, several strategies have been implemented to mitigate radiation effects in integrated circuit fabrication processes, including radiation resistant reinforcement through process technology alterations and through circuit design, layout, and architecture. In this work, multi-method has been used to reinforce the high voltage half bridge driving circuit radiation resistance ability. These methods include fabrication in a <inline-formula> <tex-math>$1.0~mu $ </tex-math></inline-formula>m 650 V silicon-on-insulator (SOI) bipolar-CMOS-DMOS (BCD) process, using ring gate devices, adding a negative feedback regulation loop, and layout optimization. This work analyzes the total irradiation dose (TID) induced performance shifts in three critical circuits of the half bridge gate driving chip and uses techniques to repair and improve the gate driving circuit performance. The three circuits include: 15/650 V level shift circuit, 650 V ring laterally-diffused-metal-oxide-semiconductor (LDMOS), and high/low side driving circuit. Experimental results show that the proposed design circuit with a chip area of <inline-formula> <tex-math>$3.88times 2.80$ </tex-math></inline-formula> mm can achieve a TID close to 100 krad(Si).","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"73 9","pages":"3639-3644"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148974742","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}

引用次数: 0

Comment on "SEU Tolerant Memory Using Error Correction Code" 对“使用纠错码的容错内存”的评析

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-08-06 DOI: 10.1109/TNS.2026.3720977

Mohamed Hichem Belkhir;Youcef Bentoutou

引用次数: 0

Proton Radiation Effects on 6T CMOS Image Sensors With Standard and Binning Mode 质子辐射对6T CMOS图像传感器的影响

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-07-30 DOI: 10.1109/TNS.2026.3717901

Mingyu Liu;Bingkai Liu;Lin Wen;Jie Feng;Zitao Zhao;Yihao Cui;Qi Guo;Yudong Li

{"title":"Proton Radiation Effects on 6T CMOS Image Sensors With Standard and Binning Mode","authors":"Mingyu Liu;Bingkai Liu;Lin Wen;Jie Feng;Zitao Zhao;Yihao Cui;Qi Guo;Yudong Li","doi":"10.1109/TNS.2026.3717901","DOIUrl":"https://doi.org/10.1109/TNS.2026.3717901","url":null,"abstract":"This article investigates proton radiation effects on a 6T CMOS image sensor (CIS) used for star sensors. The CIS supports standard mode and <inline-formula> <tex-math>$2 times 2$ </tex-math></inline-formula> binning mode. To evaluate the effects of these two operation modes on radiation response, the CIS was irradiated by 10 MeV protons, and the following parameters were characterized: photon transfer curve (PTC), mean dark current, fixed pattern noise (FPN), dynamic range (DR), and dark current random telegraph signal (DC-RTS). The results indicate that the PPD-related component accounts for approximately 69.4% of the measured value, while the remaining excess component is approximately 30.5%. This excess component is likely associated with the MN depletion region. When the CIS is operated in binning mode, the proportion of proton-induced hot pixels is significantly reduced, whereas the proportion of RTS pixels increases. A series of annealing experiments demonstrate that significant recovery of dark current can be observed up to <inline-formula> <tex-math>$160~^{circ }$ </tex-math></inline-formula>C and the annealing behaviors of the two operation modes do not exhibit noticeable differences. These results provide useful guidance for on-orbit star-pattern recognition in star sensors.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"73 9","pages":"3629-3636"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148974677","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}

引用次数: 0

A GM-YOLOv11n-Based Framework for Gamma-Ray Spectrum Peak Searching and Nuclide Identification 基于gm - yolov11n的伽玛谱峰搜索与核素识别框架

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-07-28 DOI: 10.1109/TNS.2026.3717669

Lei Xu;Rui Shi;Guang Yang;Han Ma

{"title":"A GM-YOLOv11n-Based Framework for Gamma-Ray Spectrum Peak Searching and Nuclide Identification","authors":"Lei Xu;Rui Shi;Guang Yang;Han Ma","doi":"10.1109/TNS.2026.3717669","DOIUrl":"https://doi.org/10.1109/TNS.2026.3717669","url":null,"abstract":"In critical application scenarios such as nuclear radiation monitoring, nuclear safety assessment, and radioactive waste classification, rapid and accurate nuclide identification is a core technical link in ensuring public safety and supporting emergency decision-making. As a prerequisite for nuclide identification, the accurate location of energy spectrum characteristic peaks directly determines the reliability of the identification results. However, traditional manual peak-searching methods are inefficient and highly subjective, while existing deep learning classification models lack the ability to predict fine-grained peak position information. Therefore, we propose converting 1-D energy spectrum data into 2-D images containing richer spatial information, thus reformulating the nuclide identification problem as a target detection task as a complementary approach to traditional peak-searching methods. Then, we propose a novel peak position detection method called GM-YOLOv11n. Specifically, we first use a lightweight target detection model to automatically detect characteristic peak regions; then, for overlapping peaks of nuclides with adjacent energies, we design an algorithm based on second-derivative convolution and Gaussian fitting iterative optimization to achieve effective separation of overlapping peaks, and complete the accurate matching of characteristic peak energy values with the nuclide library based on a Bayesian framework. Experimental results show that the proposed method achieves superior performance compared to baseline methods in both characteristic peak detection and nuclide matching tasks, demonstrating that the combination of deep learning feature extraction and traditional physically constrained post-processing can effectively enrich spectrum analysis methodologies, providing an effective supplementary solution for energy spectrum analysis in fields such as radiation monitoring and nuclear safety assessment.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"73 9","pages":"3773-3785"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148974735","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}

引用次数: 0

Advanced Neutron Discrimination With Diamond Detectors Using Machine Learning 使用机器学习的金刚石探测器的高级中子鉴别

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-08-20 DOI: 10.1109/TNS.2026.3725753

Romina Soledad Molina;Donny Cosic;Georgios Provatas;Maria Liz Crespo

{"title":"Advanced Neutron Discrimination With Diamond Detectors Using Machine Learning","authors":"Romina Soledad Molina;Donny Cosic;Georgios Provatas;Maria Liz Crespo","doi":"10.1109/TNS.2026.3725753","DOIUrl":"https://doi.org/10.1109/TNS.2026.3725753","url":null,"abstract":"This work explores the application of machine learning (ML) techniques for neutron/<inline-formula> <tex-math>$gamma $ </tex-math></inline-formula>-ray discrimination using transient current signals from a single-crystal (sc) chemical vapor deposition (CVD) diamond-based detector, with a focus on model robustness and hardware deployment in real-time systems. Two architectures, a multi-layer perceptron (MLP) and a 1-D convolutional neural network (1D-CNN), are evaluated across various signal domains and experimental conditions, including low-temperature operations and frequency-domain preprocessing. The performance of each architecture is evaluated using the figure of merit (FoM) method and compared to performance values obtained by classical pulse shape discrimination (PSD) techniques. In addition to assessing classification performance, the impact of processing signals with a reduced signal-to-noise ratio (SNR) caused by the operation of the detector at a lower temperature on model reliability is presented, along with an investigation of compression strategies to enable deployment on resource-constrained field-programmable gate array (FPGA) platforms. The study demonstrates that both architectures can be effectively adapted for embedded systems, balancing accuracy and efficiency for real-time signal classification tasks in radiation detection applications while achieving better FoM values than classical PSD methods.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"73 9","pages":"3732-3740"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148974767","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}

引用次数: 0

Bayesian Statistical Method for Quantifying the Uncertainty on the Measurement of Single Event Effects 量化单事件效应测量不确定性的贝叶斯统计方法

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-08-24 DOI: 10.1109/TNS.2026.3726603

Neil Rostand;Arthur Losquin;Jean-Luc Rebourg;Thomas Jarrin;Olivier Duhamel;Mohammadreza Rezaei;Juan Carlos Fabero;Juan A. Clemente;Sébastien Vigne

引用次数: 0

Study of Pulse Recovery Approaches Toward Low-Complexity PP-MVT Digitizers 低复杂度PP-MVT数字化仪脉冲恢复方法研究

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-07-03 DOI: 10.1109/TNS.2026.3709907

Yiqing Ling;Hao Lan;Ao Qiu;Junhao Yu;Yeping Zhang;Qingguo Xie

{"title":"Study of Pulse Recovery Approaches Toward Low-Complexity PP-MVT Digitizers","authors":"Yiqing Ling;Hao Lan;Ao Qiu;Junhao Yu;Yeping Zhang;Qingguo Xie","doi":"10.1109/TNS.2026.3709907","DOIUrl":"https://doi.org/10.1109/TNS.2026.3709907","url":null,"abstract":"The multivoltage threshold (MVT) method samples scintillation pulses using comparators and time-to-digital converters (TDCs) at predefined voltage thresholds, then recovers the pulse waveform through model-based fitting. We have previously proposed a novel peak-picking multivoltage threshold (PP-MVT) method to mitigate the dependence of MVT sampling performance on voltage threshold settings, while simultaneously improving energy linearity and dynamic range. However, this improvement requires additional peak-picking circuits to acquire pulse peak time and voltage information. Besides, the essential mathematical feature of pulse peaks, serving as global maxima in pulse waveforms, has not been fully exploited. To address these issues, in this study, we first introduce a novel pulse recovery approach using fixed time constants to simplify the fitting process for both MVT and PP-MVT. Building upon this, we further propose a peak-time-free pulse recovery approach to reduce circuit complexity in PP-MVT digitizers. Evaluated using an LYSO/silicon photomultiplier (SiPM) detector dataset under one- to four-threshold settings, our results demonstrate comparable energy and timing measurement performance. This study paves the way for low-complexity PP-MVT digitizers requiring only one threshold and a peak-voltage picking circuit.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"73 9","pages":"3570-3578"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148974649","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}

引用次数: 0

A Combination of Unsupervised and Supervised Machine Learning Approaches for Neutron--Gamma Discrimination in Trans-Stilbene Scintillators 反式二苯乙烯闪烁体中子-伽马判别的非监督和监督机器学习方法的结合

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-07-24 DOI: 10.1109/TNS.2026.3716755

Dimitrios Papanikolaou;Agatino Musumarra;Alfredo Pulvirenti;Maria Grazia Pellegriti

{"title":"A Combination of Unsupervised and Supervised Machine Learning Approaches for Neutron--Gamma Discrimination in Trans-Stilbene Scintillators","authors":"Dimitrios Papanikolaou;Agatino Musumarra;Alfredo Pulvirenti;Maria Grazia Pellegriti","doi":"10.1109/TNS.2026.3716755","DOIUrl":"https://doi.org/10.1109/TNS.2026.3716755","url":null,"abstract":"Traditional pulse shape discrimination (PSD) techniques using scintillation detectors in mixed neutron–gamma fields face limitations when operating at low detection thresholds. The primary challenge lies in the overlapping low-energy region, which makes it difficult to define a clear discrimination boundary. Moreover, these methods often require extensive pulse preprocessing, including careful selection of integration windows, which can reduce efficiency and increase computational complexity. This study presents a two-stage machine learning (ML) pipeline for enhanced neutron–gamma discrimination in trans-stilbene detectors. In the first stage, a Gaussian mixture model (GMM) combined with principal component analysis (PCA) performs unsupervised clustering on the full digitized pulse vectors, automatically generating neutron and gamma labels without manual threshold selection. Performance of this labeling is validated against an optimized charge integration (CI) method, demonstrating equivalent figure of merit (FoM) across the full energy range. In the second stage, supervised classifiers, a neural network (NN) and a support vector machine (SVM), are trained on six physics-based PSD features using the GMM-generated labels, achieving classification accuracies of 99.5% (NN) and 99.2% (SVM) on the training split. Cross-source validation on an independent <sup>60</sup>Co gamma source yields 99.3% gamma identification, while cross-module testing across three independent detectors demonstrates 94%–98% agreement. These results demonstrate a fully automated discrimination pipeline with direct applications to next-generation nuclear instrumentation systems requiring enhanced particle identification capability.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"73 9","pages":"3741-3751"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11623305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148974769","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}

引用次数: 0

Simulation-Driven Reinforcement Learning for PGNAA Experimental Setup Optimization PGNAA实验装置优化的仿真驱动强化学习

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-09-01 Epub Date: 2026-08-13 DOI: 10.1109/TNS.2026.3722660

Marina R. Abdelnour;Juntao Liu;A. M. Wajid;Xingzhi You;Yanyan Li;Chao Yuan;Zhiyi Liu

{"title":"Simulation-Driven Reinforcement Learning for PGNAA Experimental Setup Optimization","authors":"Marina R. Abdelnour;Juntao Liu;A. M. Wajid;Xingzhi You;Yanyan Li;Chao Yuan;Zhiyi Liu","doi":"10.1109/TNS.2026.3722660","DOIUrl":"https://doi.org/10.1109/TNS.2026.3722660","url":null,"abstract":"Optimization of the prompt gamma-ray neutron activation analysis (PGNAA) experimental setup can enhance isotopic elemental detection and provide more reliable non-destructive assays across diverse sample compositions. However, this problem involves a high-dimensional parameter space encompassing moderator material, configuration dimensions, and sample positioning and is coupled with computationally intensive Monte Carlo (MC) neutron transport simulations. As a result, it has commonly relied on manual design iterations and grid search. To address this challenge, this study introduces a novel simulation-driven reinforcement learning (RL) methodology using deep Q-learning (DQL) and online MC simulations to optimize PGNAA geometry for industrially relevant materials. The approach is applied to two neutron source configurations: a high-energy D-T source (14.1 MeV) and a low-energy D-D source (2.45 MeV). For comparison, performance is evaluated against stochastic optimization (SO) algorithms, namely, genetic algorithms (GAs) and particle swarm optimization (PSO), each maximizing a reward function that reflects both neutron thermalization efficiency at the sample position and gamma-ray detection performance at the detector. In the D-T case, DQL achieves the highest mean gamma-ray peak flux among the three algorithms, while PSO attains the highest mean reward and GA converges fastest, with no single algorithm consistently outperforming the others across all metrics; physical validation confirms DQL’s superior detection enhancement across representative PGNAA samples. In the D-D case, DQL and PSO reach statistically indistinguishable detection performance, confirmed by physical validation, while DQL completes training significantly faster than both alternatives. Taken together, these results position simulation-driven RL as a competitive framework for autonomous PGNAA geometry optimization across different neutron source energies.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"73 9","pages":"3550-3569"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148974774","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}

引用次数: 0

Burnout Susceptibility of Silicon Power Diodes When Exposed to 14-MeV Neutrons 暴露于14-MeV中子时硅功率二极管的烧蚀敏感性

IF 2.4 3区工程技术

IEEE Transactions on Nuclear Science Pub Date : 2026-08-01 Epub Date: 2026-01-28 DOI: 10.1109/TNS.2026.3658937

G. Canale Parola;S. Palazzo;G. Busatto;A. Sanseverino;E. Martano;S. G. Alberton;J. Wyss;F. Velardi

{"title":"Burnout Susceptibility of Silicon Power Diodes When Exposed to 14-MeV Neutrons","authors":"G. Canale Parola;S. Palazzo;G. Busatto;A. Sanseverino;E. Martano;S. G. Alberton;J. Wyss;F. Velardi","doi":"10.1109/TNS.2026.3658937","DOIUrl":"https://doi.org/10.1109/TNS.2026.3658937","url":null,"abstract":"Neutrons can lead to significant failures in high-voltage power devices; this article investigates the behavior of a silicon power p-i-n diode when exposed to a 14-MeV neutron beam. We present a novel methodology that combines experimental characterization and numerical simulation to examine the effects of ionic species generated by neutron-induced nuclear reactions in semiconductor materials. Experimental measurements were conducted at Frascati neutron generator (FNG) to explore the device’s failure under controlled irradiation conditions. Subsequently, to comprehend the failure mechanism, a combined simulation approach was adopted. The G4SEE Monte Carlo toolkit was first employed to simulate the interaction of 14-MeV neutrons with the silicon semiconductor material. The resulting secondary particle data were then utilized in 3-D electrothermal simulations performed using Silvaco TCAD, leveraging the cylindrical symmetry of the unit cell. The numerical model was validated by comparing the simulation results with the experimental data obtained during the neutron irradiation campaigns. Our study revealed that the (<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>, <sup>25</sup>Mg) ion pair is one of the products of the nuclear reaction that interacts most significantly with the electric field within the intrinsic region of the device. This interaction at the critical bias voltage results in a single-event burnout (SEB) of the diode, as confirmed by experimental observations and numerical analysis. Although the demonstration is based on a monoenergetic 14-MeV source, the methodology is general and can be extended to assess device response under realistic atmospheric neutron spectra.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"73 8","pages":"2865-2872"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148746888","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}

引用次数: 0