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A low noise multi-channel readout IC for X-ray cargo inspection 被引量:1
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作者 王旭 杨洪艳 +1 位作者 袁颖 吴武臣 《Journal of Semiconductors》 EI CAS CSCD 2013年第4期121-126,共6页
A low noise multi-channel readout integrated circuit (IC) which converts a detector current to analog voltage for X-ray cargo inspection is described. The readout IC provides 32 channels of a circuit having a maxi- ... A low noise multi-channel readout integrated circuit (IC) which converts a detector current to analog voltage for X-ray cargo inspection is described. The readout IC provides 32 channels of a circuit having a maxi- mum dynamic range of 15 bit and is comprised of integrator gain selection, timing generator, shift register chain, integrator array, sample/hold (S/H) stage amplifier etc. and occupies a die area of 2.7 × 13.9 mm2. It operates at It was fabricated using 0.6 μm standard CMOS process, 1 MHz, consumes 100 mW from a 5 V supply and 4.096 V as reference, and has a measured output noise of 85 μVms on 63 pF of integrator gain capacitance and 440 pF of photodiode terminal capacitance so that steel plate penetration thickness can reach more than 400 mm. 展开更多
关键词 low noise multi-channel readout IC dynamic range X-ray cargo inspection
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Material discrimination using cosmic ray muon scattering tomography with an artificial neural network 被引量:1
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作者 Weibo He Dingyue Chang +3 位作者 Rengang Shi Maobing Shuai Yingru Li Sa Xiao 《Radiation Detection Technology and Methods》 CSCD 2022年第2期254-261,共8页
Introduction Muon scattering tomography(MST)can be employed to scan cargo containers and vehicles for special nuclear materials by using cosmic muons.However,the flux of cosmic ray muons is relatively low for direct d... Introduction Muon scattering tomography(MST)can be employed to scan cargo containers and vehicles for special nuclear materials by using cosmic muons.However,the flux of cosmic ray muons is relatively low for direct detection.Thus,the detection has to be done in a short timescale with small numbers of muons to satisfy the demands of practical applications.Method In this paper,we propose an artificial neural network(ANN)algorithm for material discrimination using MST.The muon scattering angles were simulated using Geant4 to formulate the training set,and the muon scatter angles were measured by Micromegas detection system to create the test set.Results The ANN-based algorithm presented here ensures a discrimination accuracy of 98.0%between aluminum,copper and tungsten in a 5 min measurement of 4×4×4 cm^(3)blocks. 展开更多
关键词 Muon scattering tomography cargo container inspection Material discrimination Artificial neural network classifier
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