Xiangming Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiangming Sun has a total of 82 co-authors. Here are the top co-authors by number of shared publications:
Chaosong Gao
Cent China Normal Univ, Dept Phys, Wuhan 430079, Hubei, Peoples R China
4 shared publicationsLe Xiao
Cent China Normal Univ, Dept Phys, Wuhan 430079, Hubei, Peoples R China
4 shared publicationsDong Wang
Central China Normal University No.152 Luoyu Road 430079 Wuhan Hubei P.R.China
3 shared publicationsGuangming Huang
Cent China Normal Univ, Dept Phys, Wuhan 430079, Hubei, Peoples R China
3 shared publicationsHua Pei
Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, PLAC, Wuhan 430079, Peoples R China
3 shared publicationsJun Liu
Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, PLAC, Wuhan 430079, Peoples R China
3 shared publications
Latest Publications
- Open Access
Development of a Time Projection Chamber Readout with Hybrid Pixel Sensors for Beam Monitoring
To monitor the position and profile of therapeutic carbon beams in real-time, in this paper, we proposed a system called HiBeam-T. The HiBeam-T is a time projection chamber (TPC) with forty Topmetal-II- CMOS pixel sensors as its readout. Each Topmetal-II- has 72 × 72 pixels with the size of 83 μm × 83 μm. The detector consists of the charge drift region and the charge collection area. The readout electronics comprise three Readout Control Modules and one Clock Synchronization Module. This Hibeam-T has a sensitive area of 20 × 20 cm and can acquire the center of the incident beams. The test with a continuous 80.55 MeV/u 12C6+ beam shows that the measurement resolution to the beam center could reach 6.45 μm for unsaturated beam projections.
Open AccessDevelopment of a Time Projection Chamber Readout with Hybrid Pixel Sensors for Beam Monitoring
To monitor the position and profile of therapeutic carbon beams in real-time, in this paper, we proposed a system called HiBeam-T. The HiBeam-T is a time projection chamber (TPC) with forty Topmetal-II- CMOS pixel sensors as its readout. Each Topmetal-II- has 72 × 72 pixels with the size of 83 μm × 83 μm. The detector consists of the charge drift region and the charge collection area. The readout electronics comprise three Readout Control Modules and one Clock Synchronization Module. This Hibeam-T has a sensitive area of 20 × 20 cm and can acquire the center of the incident beams. The test with a continuous 80.55 MeV/u 12C6+ beam shows that the measurement resolution to the beam center could reach 6.45 μm for unsaturated beam projections.
- 1 March 2024
- Published by Springer Nature in Nuclear Science and Techniques
- Vol. 35 (3) , 1-11
Low-noise and low-power pixel sensor chip for gas pixel detectors
- 1 March 2024
- Published by Springer Nature in Nuclear Science and Techniques
- Vol. 35 (3) , 1-11
Low-noise and low-power pixel sensor chip for gas pixel detectors
- 1 February 2024
- Published by IOP Publishing in Journal of Instrumentation
- Vol. 19 (02) , C02007
A monolithic active pixel sensor with node-based, data-driven, parallel readout for the high energy physics experiment vertex detector
We present the design of a prototype MAPS sensor MIC6_V1 based on a 55 nm Quad-well CMOS Image Sensor process for the CEPC vertex detector. A new node-based, data-driven, parallel readout architecture is implemented to achieve high spatial resolution, fast readout, and low power consumption. The size of MIC6_V1 is 2.8 mm × 2.8 mm, which contains a pixel matrix of 64 rows by 64 columns, and the pixel size is 23.6 μ m × 20 μ m. The integration time is 5 μs, and the hit arrival time measurement accuracy is 10 ns.
- 1 February 2024
- Published by IOP Publishing in Journal of Instrumentation
- Vol. 19 (02) , C02007
A monolithic active pixel sensor with node-based, data-driven, parallel readout for the high energy physics experiment vertex detector
We present the design of a prototype MAPS sensor MIC6_V1 based on a 55 nm Quad-well CMOS Image Sensor process for the CEPC vertex detector. A new node-based, data-driven, parallel readout architecture is implemented to achieve high spatial resolution, fast readout, and low power consumption. The size of MIC6_V1 is 2.8 mm × 2.8 mm, which contains a pixel matrix of 64 rows by 64 columns, and the pixel size is 23.6 μ m × 20 μ m. The integration time is 5 μs, and the hit arrival time measurement accuracy is 10 ns.
- Open Access
Towards Lightweight Neural Networks for Garbage Object Detection
In recent years, garbage classification has become a hot topic in China, and legislation on garbage classification has been proposed. Proper garbage classification and improving the recycling rate of garbage can protect the environment and save resources. In order to effectively achieve garbage classification, a lightweight garbage object detection model based on deep learning techniques was designed and developed in this study, which can locate and classify garbage objects in real-time using embedded devices. Focusing on the problems of low accuracy and poor real-time performances in garbage classification, we proposed a lightweight garbage object detection model, YOLOG (YOLO for garbage detection), which is based on accurate local receptive field dilation and can run on embedded devices at high speed and with high performance. YOLOG improves on YOLOv4 in three key ways, including the design of DCSPResNet with accurate local receptive field expansion based on dilated–deformable convolution, network structure simplification, and the use of new activation functions. We collected the domestic garbage image dataset, then trained and tested the model on it. Finally, in order to compare the performance difference between YOLOG and existing state-of-the-art algorithms, we conducted comparison experiments using a uniform data set training model. The experimental results showed that YOLOG achieved of 94.58% and computation of 6.05 Gflops, thus outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in terms of comprehensive performance indicators. The network proposed in this paper can detect domestic garbage accurately and rapidly, provide a foundation for future academic research and engineering applications.Open AccessTowards Lightweight Neural Networks for Garbage Object Detection
In recent years, garbage classification has become a hot topic in China, and legislation on garbage classification has been proposed. Proper garbage classification and improving the recycling rate of garbage can protect the environment and save resources. In order to effectively achieve garbage classification, a lightweight garbage object detection model based on deep learning techniques was designed and developed in this study, which can locate and classify garbage objects in real-time using embedded devices. Focusing on the problems of low accuracy and poor real-time performances in garbage classification, we proposed a lightweight garbage object detection model, YOLOG (YOLO for garbage detection), which is based on accurate local receptive field dilation and can run on embedded devices at high speed and with high performance. YOLOG improves on YOLOv4 in three key ways, including the design of DCSPResNet with accurate local receptive field expansion based on dilated–deformable convolution, network structure simplification, and the use of new activation functions. We collected the domestic garbage image dataset, then trained and tested the model on it. Finally, in order to compare the performance difference between YOLOG and existing state-of-the-art algorithms, we conducted comparison experiments using a uniform data set training model. The experimental results showed that YOLOG achieved of 94.58% and computation of 6.05 Gflops, thus outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in terms of comprehensive performance indicators. The network proposed in this paper can detect domestic garbage accurately and rapidly, provide a foundation for future academic research and engineering applications. - 1 August 2022
- Published by IOP Publishing in Journal of Instrumentation
- Vol. 17 (08) , C08021
A 14 Gbps VCSEL driving ASIC in 55 nm for NICA multi purpose detector project
This paper presents the design and the test results of a 14 Gbps VCSEL driving ASIC with a novel output driver structure fabricated in a 55 nm CMOS process. It consists of an equalizer stage, a limiting amplifier stage and an output driver stage. The equalizer stage uses the CTLE structure to compensate the high frequency losses at the PCB traces, bonding wires and input pads. To meet both the gain/bandwidth requirements and the area restriction, the limiting amplifier stage adopts the inductor-shared peaking technology. To increase the voltage headroom of the output driver stage and improve the bandwidth, a novel output driver structure using the on-chip AC coupling, the stacked current source and the double feedforward compensation technique is proposed. This ASIC has been integrated with the VCSEL array in a customized optical module for the optical test. The test results show the wide-open 14 Gbps optical eye diagram with a measured RMS jitter of 2.7 ps and a peak-to-peak jitter of 17.4 ps, respectively.
- 1 August 2022
- Published by IOP Publishing in Journal of Instrumentation
- Vol. 17 (08) , C08021
A 14 Gbps VCSEL driving ASIC in 55 nm for NICA multi purpose detector project
This paper presents the design and the test results of a 14 Gbps VCSEL driving ASIC with a novel output driver structure fabricated in a 55 nm CMOS process. It consists of an equalizer stage, a limiting amplifier stage and an output driver stage. The equalizer stage uses the CTLE structure to compensate the high frequency losses at the PCB traces, bonding wires and input pads. To meet both the gain/bandwidth requirements and the area restriction, the limiting amplifier stage adopts the inductor-shared peaking technology. To increase the voltage headroom of the output driver stage and improve the bandwidth, a novel output driver structure using the on-chip AC coupling, the stacked current source and the double feedforward compensation technique is proposed. This ASIC has been integrated with the VCSEL array in a customized optical module for the optical test. The test results show the wide-open 14 Gbps optical eye diagram with a measured RMS jitter of 2.7 ps and a peak-to-peak jitter of 17.4 ps, respectively.
- 1 January 2022
- Published by IOP Publishing in Journal of Instrumentation
- Vol. 17 (01) , C01027
LDLA14: a 14 Gbps optical transceiver ASIC in 55 nm for NICA multi purpose detector project
This paper presents the design and test results of a 14 Gbps optical transceiver ASIC (LDLA14) fabricated in a 55 nm CMOS technology for NICA Multi Purpose Detector (MPD) project. The LDLA14 is a single-channel bidirectional (1Tx + 1Rx) optical transceiver ASIC, including a Laser Driver (LD) module and a Limiting Amplifier (LA) module. It would drive the Vertical Cavity Surface Emitting Laser (VCSEL) of Transmitter Optical Sub-Assembly (TOSA) and receive signals from Photo Diode (PD) of Receiver Optical Sub-Assembly (ROSA), respectively. In the LDLA14, a novel structure of capacitive coupling pre-emphasis is proposed in the output driver of LD to obtain peaking effect without sacrifice the modulation current swing. A shared inductor technology and a Continuous Time Linear Equalizer (CTLE) pre-emphasis structure are added in the output buffer of LA to improve the quality of the output eye diagram. The dimension of LDLA14 is 1.5 mm × 1.3 mm, and the power consumption is 178 mW. The Peak-to-Peak Jitter (PPJ) and Root-Mean-Square Jitter (RMSJ) of the 14 Gbps optical eye diagram of LD in the Tx direction are 22.5 ps and 3.5 ps, respectively. The PPJ and RMSJ of the 14 Gbps electrical eye diagram of LA in the Rx direction are 23.1 ps and 4.7 ps, respectively. The BER tests have been conducted in Tx, Rx directions and the Tx-Rx loop condition, and the BER less than 10−12 is achieved in all tests.
- 1 January 2022
- Published by IOP Publishing in Journal of Instrumentation
- Vol. 17 (01) , C01027
LDLA14: a 14 Gbps optical transceiver ASIC in 55 nm for NICA multi purpose detector project
This paper presents the design and test results of a 14 Gbps optical transceiver ASIC (LDLA14) fabricated in a 55 nm CMOS technology for NICA Multi Purpose Detector (MPD) project. The LDLA14 is a single-channel bidirectional (1Tx + 1Rx) optical transceiver ASIC, including a Laser Driver (LD) module and a Limiting Amplifier (LA) module. It would drive the Vertical Cavity Surface Emitting Laser (VCSEL) of Transmitter Optical Sub-Assembly (TOSA) and receive signals from Photo Diode (PD) of Receiver Optical Sub-Assembly (ROSA), respectively. In the LDLA14, a novel structure of capacitive coupling pre-emphasis is proposed in the output driver of LD to obtain peaking effect without sacrifice the modulation current swing. A shared inductor technology and a Continuous Time Linear Equalizer (CTLE) pre-emphasis structure are added in the output buffer of LA to improve the quality of the output eye diagram. The dimension of LDLA14 is 1.5 mm × 1.3 mm, and the power consumption is 178 mW. The Peak-to-Peak Jitter (PPJ) and Root-Mean-Square Jitter (RMSJ) of the 14 Gbps optical eye diagram of LD in the Tx direction are 22.5 ps and 3.5 ps, respectively. The PPJ and RMSJ of the 14 Gbps electrical eye diagram of LA in the Rx direction are 23.1 ps and 4.7 ps, respectively. The BER tests have been conducted in Tx, Rx directions and the Tx-Rx loop condition, and the BER less than 10−12 is achieved in all tests.
- Open Access
A deep learning approach to multi-track location and orientation in gaseous drift chambers
Accurate measuring the location and orientation of individual particles in a beam monitoring system is of particular interest to researchers in multiple disciplines. Among feasible methods, gaseous drift chambers with hybrid pixel sensors have the great potential to realize long-term stable measurement with considerable precision. In this paper, we introduce deep learning to analyze patterns in the beam projection image to facilitate three-dimensional reconstruction of particle tracks. We propose an end-to-end neural network based on segmentation and fitting for feature extraction and regression. Two segmentation branches, named binary segmentation and semantic segmentation, perform initial track determination and pixel-track association. Then pixels are assigned to multiple tracks, and a weighted least squares fitting is implemented with full back-propagation. Besides, we introduce a center-angle measure to judge the precision of location and orientation by combining two separate factors. The initial position resolution achieves 8.8 $\mu m$ for the single track and 11.4 $\mu m$ (15.2 $\mu m$) for the 1-3 tracks (1-5 tracks), and the angle resolution achieves 0.15$^{\circ}$ and 0.21$^{\circ}$ (0.29$^{\circ}$) respectively. These results show a significant improvement in accuracy and multi-track compatibility compared to traditional methods.Open AccessA deep learning approach to multi-track location and orientation in gaseous drift chambers
Accurate measuring the location and orientation of individual particles in a beam monitoring system is of particular interest to researchers in multiple disciplines. Among feasible methods, gaseous drift chambers with hybrid pixel sensors have the great potential to realize long-term stable measurement with considerable precision. In this paper, we introduce deep learning to analyze patterns in the beam projection image to facilitate three-dimensional reconstruction of particle tracks. We propose an end-to-end neural network based on segmentation and fitting for feature extraction and regression. Two segmentation branches, named binary segmentation and semantic segmentation, perform initial track determination and pixel-track association. Then pixels are assigned to multiple tracks, and a weighted least squares fitting is implemented with full back-propagation. Besides, we introduce a center-angle measure to judge the precision of location and orientation by combining two separate factors. The initial position resolution achieves 8.8 $\mu m$ for the single track and 11.4 $\mu m$ (15.2 $\mu m$) for the 1-3 tracks (1-5 tracks), and the angle resolution achieves 0.15$^{\circ}$ and 0.21$^{\circ}$ (0.29$^{\circ}$) respectively. These results show a significant improvement in accuracy and multi-track compatibility compared to traditional methods. - Open Access
A beam monitor using silicon pixel sensors for hadron therapy
We report the design and test results of a beam monitor developed for online monitoring in hadron therapy. The beam monitor uses eight silicon pixel sensors, \textit{Topmetal-${II}^-$}, as the anode array. \textit{Topmetal-${II}^-$} is a charge sensor designed in a CMOS 0.35 $\mu$m technology. Each \textit{Topmetal-${II}^-$} sensor has $72\times72$ pixels and the pixel size is $83\times83$ $\mu$m$^2$. In our design, the beam passes through the beam monitor without hitting the electrodes, making the beam monitor especially suitable for monitoring heavy ion beams. This design also reduces radiation damage to the beam monitor itself. The beam monitor is tested with a carbon ion beam at the Heavy Ion Research Facility in Lanzhou (HIRFL). Results indicate that the beam monitor can measure position, incidence angle and intensity of the beam with a position resolution better than 20 $\mu$m, angular resolution about 0.5$^\circ$ and intensity statistical accuracy better than 2$\%$.Open AccessA beam monitor using silicon pixel sensors for hadron therapy
We report the design and test results of a beam monitor developed for online monitoring in hadron therapy. The beam monitor uses eight silicon pixel sensors, \textit{Topmetal-${II}^-$}, as the anode array. \textit{Topmetal-${II}^-$} is a charge sensor designed in a CMOS 0.35 $\mu$m technology. Each \textit{Topmetal-${II}^-$} sensor has $72\times72$ pixels and the pixel size is $83\times83$ $\mu$m$^2$. In our design, the beam passes through the beam monitor without hitting the electrodes, making the beam monitor especially suitable for monitoring heavy ion beams. This design also reduces radiation damage to the beam monitor itself. The beam monitor is tested with a carbon ion beam at the Heavy Ion Research Facility in Lanzhou (HIRFL). Results indicate that the beam monitor can measure position, incidence angle and intensity of the beam with a position resolution better than 20 $\mu$m, angular resolution about 0.5$^\circ$ and intensity statistical accuracy better than 2$\%$.