Prototype of single-event effect localization system with CMOS pixel sensor

Liu, Jun; Zhang, Zhuo; Wang, Dong; Zhou, Shuiqin; Sun, X.; Ren, Weiping; You, Bihui; Gao, C.; Xiao, Le; Yang, P.; Guo, D.; Zhou, Wei; Zhao, C.; Wang, Min

doi:10.1007/s41365-022-01128-5

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…The DAQ system controls and reads data from the two chips and then transfers the data to a computer. A detailed description of this experiment is presented in [8].…”

Section: Cluster Data Generation In Heavy-ion Campaignsmentioning

confidence: 99%

“…In addition, some newly designed MAPSs [5,6] also record the energy and timing information of the particle hits. The typical MAPS-based detectors at HIRFL and HIAF are vertex and tracking detectors in Electron-ion colliders in China (EicC) [7] and the beam-positioning system at physics terminals [8]. Besides these benefits, adopting MAPS technology increases the data volume.…”

Section: Introductionmentioning

confidence: 99%

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Cluster-Locating Algorithm Based on Deep Learning for Silicon Pixel Sensors

Mai

Yang

Wang

et al. 2023

Sensors

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The application of silicon pixel sensors provides an excellent signal-to-noise ratio, spatial resolution, and readout speed in particle physics experiments. Therefore, high-performance cluster-locating technology is highly required in CMOS-sensor-based systems to compress the data volume and improve the accuracy and speed of particle detection. Object detection techniques using deep learning technology demonstrate significant potential for achieving high-performance particle cluster location. In this study, we constructed and compared the performance of one-stage detection algorithms with the representative YOLO (You Only Look Once) framework and two-stage detection algorithms with an RCNN (region-based convolutional neural network). In addition, we also compared transformer-based backbones and CNN-based backbones. The dataset was obtained from a heavy-ion test on a Topmetal-M silicon pixel sensor at HIRFL. Heavy-ion tests were performed on the Topmetal-M silicon pixel sensor to establish the dataset for training and validation. In general, we achieved state-of-the-art results: 68.0% AP (average precision) at a speed of 10.04 FPS (Frames Per Second) on Tesla V100. In addition, the detection efficiency is on the same level as that of the traditional Selective Search approach, but the speed is higher.

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“…The DAQ system controls and reads data from the two chips and then transfers the data to a computer. A detailed description of this experiment is presented in [8].…”

Section: Cluster Data Generation In Heavy-ion Campaignsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cluster-Locating Algorithm Based on Deep Learning for Silicon Pixel Sensors

Mai

Yang

Wang

et al. 2023

Sensors

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“…However, CMOS devices are often subject to collisions with high-energy protons and heavy ions from the cosmic space environment. Therefore, CMOS devices are susceptible to Single Event Effect (SEE) [3][4][5][6]. In particular, Single Event Latch-up (SEL), a special SEE, can alter devices' currents and even cause devices to burn up in severe cases [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Study of Single Event Latch-Up Hardness for CMOS Devices with a Resistor in Front of DC-DC Converter

Xin

Zhu

et al. 2023

Electronics

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Bulk silicon Complementary Metal Oxide Semiconductor (CMOS) devices have distinct single event latch-up (SEL) problems in aerospace. Therefore, it is essential that CMOS devices are designed with appropriate circuit-level methods. Traditional resistor hardness satisfies the current aerospace trend of low cost, high performance, and miniaturization. Therefore conventional resistor hardness is often applied in circuit-level designs due to the reduction of latch-up current. In circuits containing a DC-DC buck converter, the resistor is connected to the back of the converter in the traditional method. However, the traditional method is unable to take devices out of the latch-up owing to the small resistance range. To solve this problem, the paper proposes an improved design for the resistor in front of the DC-DC buck converter. The proposed method enables the devices to exit the latch-up by increasing the resistance range according to the input characteristic of the DC-DC buck converter. The paper quantifies the range of the resistor through the parametric model containing the resistor and the DC-DC buck converter. Two CMOS devices are chosen for pulsed laser experiments, verifying that the proposed method increases the resistance ranges by 300% to 400% compared to the conventional method. It is also demonstrated that the proposed method exits the devices from latch-up within the resistor ranges. That is, the resistance ranges of 34 Ω~41 Ω and 51 Ω~56 Ω reduce the latch-up currents of the devices to below holding currents of 72.1 mA and 24.2 mA, respectively.

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Electronics system for the cosmic X-ray polarization detector

Wang

Chen

et al. 2023

NUCL SCI TECH

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Prototype of single-event effect localization system with CMOS pixel sensor

Cited by 8 publications

References 21 publications

Cluster-Locating Algorithm Based on Deep Learning for Silicon Pixel Sensors

Cluster-Locating Algorithm Based on Deep Learning for Silicon Pixel Sensors

Study of Single Event Latch-Up Hardness for CMOS Devices with a Resistor in Front of DC-DC Converter

Electronics system for the cosmic X-ray polarization detector

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