The silicon pixel sensor（SPS） is one of the key components of hybrid pixel single-photon-counting detectors for synchrotron radiation X-ray detection（SRD）. In this paper, the design, fabrication, and characterizat...The silicon pixel sensor（SPS） is one of the key components of hybrid pixel single-photon-counting detectors for synchrotron radiation X-ray detection（SRD）. In this paper, the design, fabrication, and characterization of SPSs for single beam X-ray photon detection is reported. The designed pixel sensor is a p＋-in-n structure with guard-ring structures operated in full-depletion mode and is fabricated on 4-inch, N type, 320 μm thick, high-resistivity silicon wafers by a general Si planar process. To achieve high energy resolution of X-rays and obtain low dark current and high breakdown voltage as well as appropriate depletion voltage of the SPS, a series of technical optimizations of device structure and fabrication process are explored. With optimized device structure and fabrication process,excellent SPS characteristics with dark current of 2 n A/cm^2, full depletion voltage 〈 50 V and breakdown voltage〉 150 V are achieved. The fabricated SPSs are wire bonded to ASIC circuits and tested for the performance of X-ray response to the 1W2 B synchrotron beam line of the Beijing Synchrotron Radiation Facility. The measured S-curves for SRD demonstrate a high discrimination for different energy X-rays. The extracted energy resolution is high（〈 20% for X-ray photon energy 〉 10 keV） and the linear properties between input photo energy and the equivalent generator amplitude are well established. It confirmed that the fabricated SPSs have a good energy linearity and high count rate with the optimized technologies. The technology is expected to have a promising application in the development of a large scale SRD system for the Beijing Advanced Photon Source.展开更多
Identifying sensitive areas in integrated circuits susceptible to single-event effects(SEE)is crucial for improving radiation hardness.This study presents an online multi-track location(OML)framework to enhance the hi...Identifying sensitive areas in integrated circuits susceptible to single-event effects(SEE)is crucial for improving radiation hardness.This study presents an online multi-track location(OML)framework to enhance the high-resolution online trajectory detection for the Hi’Beam-SEE system,which aims to localize SEE-sensitive positions on the IC at the micrometer scale and in real time.We employed a reparameterization method to accelerate the inference speed,merging the branches of the backbone of the location in the deployment scenario.Additionally,we designed an irregular convolution kernel,an attention mechanism,and a fused loss function to improve the positioning accuracy.OML demonstrates exceptional realtime processing capabilities,achieving a positioning accuracy of 1.83μm in processing data generated by the Hi’Beam-SEE system at 163 frames per second per GPU.展开更多
To improve the efficiency and accuracy of single-event effect(SEE)research at the Heavy Ion Research Facility at Lanzhou,Hi’Beam-SEE must precisely localize the position at which each heavy ion hitting the integrated...To improve the efficiency and accuracy of single-event effect(SEE)research at the Heavy Ion Research Facility at Lanzhou,Hi’Beam-SEE must precisely localize the position at which each heavy ion hitting the integrated circuit(IC)causes SEE.In this study,we propose a fast multi-track location(FML)method based on deep learning to locate the position of each particle track with high speed and accuracy.FML can process a vast amount of data supplied by Hi’Beam-SEE online,revealing sensitive areas in real time.FML is a slot-based object-centric encoder-decoder structure in which each slot can learn the location information of each track in the image.To make the method more accurate for real data,we designed an algorithm to generate a simulated dataset with a distribution similar to that of the real data,which was then used to train the model.Extensive comparison experiments demonstrated that the FML method,which has the best performance on simulated datasets,has high accuracy on real datasets as well.In particular,FML can reach 238 fps and a standard error of 1.6237μm.This study discusses the design and performance of FML.展开更多
基金Supported by Prefabrication Research of Beijing Advanced Photon Source(R&D for BAPS)National Natural Science Foundation of China(11335010)
文摘The silicon pixel sensor（SPS） is one of the key components of hybrid pixel single-photon-counting detectors for synchrotron radiation X-ray detection（SRD）. In this paper, the design, fabrication, and characterization of SPSs for single beam X-ray photon detection is reported. The designed pixel sensor is a p＋-in-n structure with guard-ring structures operated in full-depletion mode and is fabricated on 4-inch, N type, 320 μm thick, high-resistivity silicon wafers by a general Si planar process. To achieve high energy resolution of X-rays and obtain low dark current and high breakdown voltage as well as appropriate depletion voltage of the SPS, a series of technical optimizations of device structure and fabrication process are explored. With optimized device structure and fabrication process,excellent SPS characteristics with dark current of 2 n A/cm^2, full depletion voltage 〈 50 V and breakdown voltage〉 150 V are achieved. The fabricated SPSs are wire bonded to ASIC circuits and tested for the performance of X-ray response to the 1W2 B synchrotron beam line of the Beijing Synchrotron Radiation Facility. The measured S-curves for SRD demonstrate a high discrimination for different energy X-rays. The extracted energy resolution is high（〈 20% for X-ray photon energy 〉 10 keV） and the linear properties between input photo energy and the equivalent generator amplitude are well established. It confirmed that the fabricated SPSs have a good energy linearity and high count rate with the optimized technologies. The technology is expected to have a promising application in the development of a large scale SRD system for the Beijing Advanced Photon Source.
基金supported by the National Natural Science Foundation of China(Nos.U2032209,12222512,12375193,12305210)the National Key Research and Development Program of China(No.2021YFA1601300)the CAS“Light of West China”Program,the CAS Pioneer Hundred Talent Program,the Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030008).
文摘Identifying sensitive areas in integrated circuits susceptible to single-event effects(SEE)is crucial for improving radiation hardness.This study presents an online multi-track location(OML)framework to enhance the high-resolution online trajectory detection for the Hi’Beam-SEE system,which aims to localize SEE-sensitive positions on the IC at the micrometer scale and in real time.We employed a reparameterization method to accelerate the inference speed,merging the branches of the backbone of the location in the deployment scenario.Additionally,we designed an irregular convolution kernel,an attention mechanism,and a fused loss function to improve the positioning accuracy.OML demonstrates exceptional realtime processing capabilities,achieving a positioning accuracy of 1.83μm in processing data generated by the Hi’Beam-SEE system at 163 frames per second per GPU.
基金supported by the National Natural Science Foundation of China (Nos.U2032209,11975292,12222512)the National Key Research and Development Program of China (2021YFA1601300)+2 种基金the CAS“Light of West China”Programthe CAS Pioneer Hundred Talent Programthe Guangdong Major Project of Basic and Applied Basic Research (No.2020B0301030008)。
文摘To improve the efficiency and accuracy of single-event effect(SEE)research at the Heavy Ion Research Facility at Lanzhou,Hi’Beam-SEE must precisely localize the position at which each heavy ion hitting the integrated circuit(IC)causes SEE.In this study,we propose a fast multi-track location(FML)method based on deep learning to locate the position of each particle track with high speed and accuracy.FML can process a vast amount of data supplied by Hi’Beam-SEE online,revealing sensitive areas in real time.FML is a slot-based object-centric encoder-decoder structure in which each slot can learn the location information of each track in the image.To make the method more accurate for real data,we designed an algorithm to generate a simulated dataset with a distribution similar to that of the real data,which was then used to train the model.Extensive comparison experiments demonstrated that the FML method,which has the best performance on simulated datasets,has high accuracy on real datasets as well.In particular,FML can reach 238 fps and a standard error of 1.6237μm.This study discusses the design and performance of FML.
