Simulation study of energy resolution with changing pixel size for radon monitor based on Topmetal-Ⅱ~- TPC
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摘要
In this paper, we study how pixel size influences energy resolution for a proposed pixelated detector—a high sensitivity, low cost, and real-time radon monitor based on a Topmetal-Ⅱ~- time projection chamber(TPC). This monitor was designed to improve spatial resolution for detecting radon alpha particles using Topmetal-Ⅱ~- sensors assembled by a 0.35 lm CMOS integrated circuit process.Owing to concerns that small pixel size might have the side effect of worsening energy resolution due to lower signalto-noise ratio, a Geant4-based simulation was used to investigate the dependence of energy resolution on pixel sizes ranging from 60 to 600 lm. A non-monotonic trend in this region shows the combined effect of pixel size and threshold on pixels, analyzed by introducing an empirical expression. Pixel noise contributes 50 keV full-width at half-maximum energy resolution for 400 lm pixel size at 1–4σ threshold that is comparable to the energy resolution caused by energy fluctuations in the TPC ionization process( ~20 keV). The total energy resolution after combining both factors is estimated to be 54 keV for a pixel size of 400 lm at 1–4σ threshold. The analysis presented in this paper would help choosing suitable pixel size for future pixelated detectors.
In this paper, we study how pixel size influences energy resolution for a proposed pixelated detector—a high sensitivity, low cost, and real-time radon monitor based on a Topmetal-Ⅱ~- time projection chamber(TPC). This monitor was designed to improve spatial resolution for detecting radon alpha particles using Topmetal-Ⅱ~- sensors assembled by a 0.35 lm CMOS integrated circuit process.Owing to concerns that small pixel size might have the side effect of worsening energy resolution due to lower signalto-noise ratio, a Geant4-based simulation was used to investigate the dependence of energy resolution on pixel sizes ranging from 60 to 600 lm. A non-monotonic trend in this region shows the combined effect of pixel size and threshold on pixels, analyzed by introducing an empirical expression. Pixel noise contributes 50 keV full-width at half-maximum energy resolution for 400 lm pixel size at 1–4σ threshold that is comparable to the energy resolution caused by energy fluctuations in the TPC ionization process( ~20 keV). The total energy resolution after combining both factors is estimated to be 54 keV for a pixel size of 400 lm at 1–4σ threshold. The analysis presented in this paper would help choosing suitable pixel size for future pixelated detectors.
In this paper, we study how pixel size influences energy resolution for a proposed pixelated detector—a high sensitivity, low cost, and real-time radon monitor based on a Topmetal-Ⅱ~- time projection chamber(TPC). This monitor was designed to improve spatial resolution for detecting radon alpha particles using Topmetal-Ⅱ~- sensors assembled by a 0.35 lm CMOS integrated circuit process.Owing to concerns that small pixel size might have the side effect of worsening energy resolution due to lower signalto-noise ratio, a Geant4-based simulation was used to investigate the dependence of energy resolution on pixel sizes ranging from 60 to 600 lm. A non-monotonic trend in this region shows the combined effect of pixel size and threshold on pixels, analyzed by introducing an empirical expression. Pixel noise contributes 50 keV full-width at half-maximum energy resolution for 400 lm pixel size at 1–4σ threshold that is comparable to the energy resolution caused by energy fluctuations in the TPC ionization process( ~20 keV). The total energy resolution after combining both factors is estimated to be 54 keV for a pixel size of 400 lm at 1–4σ threshold. The analysis presented in this paper would help choosing suitable pixel size for future pixelated detectors.
引文
1.World Health Organization,WHO Handbook on Indoor Radon:APublic Health Perspective(WHO Press,Geneva,2009).http://www.who.int/ionizing_radiation/env/9789241547673/en/.Accessed 23 Aug 2018
2.R.C.Bruno,Sources of indoor radon in houses:a review.J.Air Pollut.Control Assoc.33(2),105-109(1983).https://doi.org/10.1080/00022470.1983.10465550
3.F.Bochicchio,J.P.McLaughling,S.Piermattei,Report No 15:radon in indoor air(EUR 16123 EN),in European Collaborative Action:Indoor Air Quality and Its Impact on Man(formerly Cost Project 613)-Environment and Quality of Life(European Commission,Luxembourg,1995).http://www.aivc.org/resource/eca-15-radon-indoor-air?collection=34251.Accessed 23 Aug2018
4.M.Baskaran,Radon measurement techniques,in Radon:ATracer for Geological,Geophysical and Geochemical Studies Springer Geochemistry(Springer,Cham,2016),pp.15-35.https://doi.org/10.1007/978-3-319-21329-3_2
5.A.Bosi,L.Bidinelli,D.Saguatti et al.,Performance of a radon sensor based on a BJT detector on high-resistivity silicon,in 2012IEEE Nuclear Science Symposium and Medical Imaging Conference Record(NSS/MIC),Anaheim,CA(2012),pp.266-268.https://doi.org/10.1109/NSSMIC.2012.6551105
6.P.Ashokkumar,B.K.Sahoo,A.Raman et al.,Development and characterisation of a silicon PIN diode array based highly sensitive portable continuous radon monitor.J.Radiol.Prot.34,149-160(2014).https://doi.org/10.1088/0952-4746/34/1/149
7.J.Sand,S.Ihantola,K.Pera¨ja¨rvi et al.,Optical detection of radon decay in air.Sci.Rep.6,21532(2016).https://doi.org/10.1038/srep21532
8.S.Higueret,D.Husson,T.D.Le et al.,Electronic radon monitoring with the CMOS System-on-Chip AlphaRad.Nucl.Instrum.Meth.A 584(2-3),412-417(2008).https://doi.org/10.1016/j.nima.2007.10.034
9.A.J.H.Ross,R.H.Griffin,N.G.Tarr,Radon monitor using alphadetecting CMOS IC,in 2016 IEEE Sensors Applications Symposium(SAS),Catania(2016),pp.1-5.https://doi.org/10.1109/SAS.2016.7479856
10.A.Nachab,D.Husson,A.Nourreddine et al.,First measurement of222Rn activity with a CMOS active pixel sensor.Nucl.Instrum.Methods Phys.Res.B 225(3),418-422(2004).https://doi.org/10.1016/j.nimb.2004.05.017
11.M.Caresana,L.Garlati,F.Murtas et al.,Real-time measurements of radon activity with the Timepix-based RADONLITE and RADONPIX detectors.J.Instrum.9(11),P11023(2014)
12.B.D.McNally,S.Coleman,J.T.Harris,et al.,Improving the limits of detection of low background alpha emission measurements,in AIP Conference Proceedings,vol.1921,no.1(AIPPublishing,2018),p.30001.https://doi.org/10.1063/1.5018988
13.M.An,C.Chen,C.Gao et al.,A low-noise CMOS pixel direct charge sensor,Topmetal-IIà.Nucl.Instrum.Meth.A 810,144-150(2016).https://doi.org/10.1016/j.nima.2015.11.153
14.S.Agostinelli,J.Allison,K.Amako et al.,Geant4-a simulation toolkit.Nucl.Instrum.Meth.A 506,250-303(2003).https://doi.org/10.1016/S0168-9002(03)01368-8
15.G.Charpak,P.Benaben,P.Breuil,et al.,Performance of wiretype Rn detectors operated with gas gain in ambient air in view of its possible application to early earthquake predictions(2010).arXiv:1002.4732
16.G.Boissonnat,J.M.Fontbonne,J.Colin,et al.,Measurement of ion and electron drift velocity and electronic attachment in air for ionization chambers(2016).arXiv:1609.0374.Accessed 23 Aug2018
17.S.Biagi,Magboltz-Transport of Electrons in Gas Mixtures.CERN Program Library(2000)
18.C.Gao,G.Huang,X.Sun,Topmetal-IIà:a direct charge sensor for high energy physics and imaging applications.J.Instrum.11(1),C01053(2016)
19.T.J.O’Shea,J.Corgan,T.C.Clancy,Convolutional radio modulation recognition networks,in Engineering Applications of Neural Networks,vol.629,EANN 2016.Communications in Computer and Information Science,ed.by C.Jayne,L.Iliadis(Springer,Cham,2016),pp.213-226.https://doi.org/10.1007/978-3-319-44188-7_16
20.Y.Zhu,Q.Ouyang,Y.Mao,A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.BMC Bioinform.18,348(2017).https://doi.org/10.1186/s12859-017-1757-y
21.R.Li,T.Zeng,H.Peng et al.,Deep learning segmentation of optical microscopy images improves 3-D neuron reconstruction.IEEE Trans.Med.Imaging 36,1533-1541(2017).https://doi.org/10.1109/TMI.2017.2679713
22.R.Acciarri,C.Adams,R.An et al.,Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber.J.Instrum.12,P03011(2017)
23.M.An,C.Chen,C.Gao et al.,A low-noise CMOS pixel direct charge sensor Topmetal-IIa for low background and low ratedensity experiments.Nucl.Instrum.Meth.A 810,144-150(2016).https://doi.org/10.1016/j.nima.2015.11.153
2.R.C.Bruno,Sources of indoor radon in houses:a review.J.Air Pollut.Control Assoc.33(2),105-109(1983).https://doi.org/10.1080/00022470.1983.10465550
3.F.Bochicchio,J.P.McLaughling,S.Piermattei,Report No 15:radon in indoor air(EUR 16123 EN),in European Collaborative Action:Indoor Air Quality and Its Impact on Man(formerly Cost Project 613)-Environment and Quality of Life(European Commission,Luxembourg,1995).http://www.aivc.org/resource/eca-15-radon-indoor-air?collection=34251.Accessed 23 Aug2018
4.M.Baskaran,Radon measurement techniques,in Radon:ATracer for Geological,Geophysical and Geochemical Studies Springer Geochemistry(Springer,Cham,2016),pp.15-35.https://doi.org/10.1007/978-3-319-21329-3_2
5.A.Bosi,L.Bidinelli,D.Saguatti et al.,Performance of a radon sensor based on a BJT detector on high-resistivity silicon,in 2012IEEE Nuclear Science Symposium and Medical Imaging Conference Record(NSS/MIC),Anaheim,CA(2012),pp.266-268.https://doi.org/10.1109/NSSMIC.2012.6551105
6.P.Ashokkumar,B.K.Sahoo,A.Raman et al.,Development and characterisation of a silicon PIN diode array based highly sensitive portable continuous radon monitor.J.Radiol.Prot.34,149-160(2014).https://doi.org/10.1088/0952-4746/34/1/149
7.J.Sand,S.Ihantola,K.Pera¨ja¨rvi et al.,Optical detection of radon decay in air.Sci.Rep.6,21532(2016).https://doi.org/10.1038/srep21532
8.S.Higueret,D.Husson,T.D.Le et al.,Electronic radon monitoring with the CMOS System-on-Chip AlphaRad.Nucl.Instrum.Meth.A 584(2-3),412-417(2008).https://doi.org/10.1016/j.nima.2007.10.034
9.A.J.H.Ross,R.H.Griffin,N.G.Tarr,Radon monitor using alphadetecting CMOS IC,in 2016 IEEE Sensors Applications Symposium(SAS),Catania(2016),pp.1-5.https://doi.org/10.1109/SAS.2016.7479856
10.A.Nachab,D.Husson,A.Nourreddine et al.,First measurement of222Rn activity with a CMOS active pixel sensor.Nucl.Instrum.Methods Phys.Res.B 225(3),418-422(2004).https://doi.org/10.1016/j.nimb.2004.05.017
11.M.Caresana,L.Garlati,F.Murtas et al.,Real-time measurements of radon activity with the Timepix-based RADONLITE and RADONPIX detectors.J.Instrum.9(11),P11023(2014)
12.B.D.McNally,S.Coleman,J.T.Harris,et al.,Improving the limits of detection of low background alpha emission measurements,in AIP Conference Proceedings,vol.1921,no.1(AIPPublishing,2018),p.30001.https://doi.org/10.1063/1.5018988
13.M.An,C.Chen,C.Gao et al.,A low-noise CMOS pixel direct charge sensor,Topmetal-IIà.Nucl.Instrum.Meth.A 810,144-150(2016).https://doi.org/10.1016/j.nima.2015.11.153
14.S.Agostinelli,J.Allison,K.Amako et al.,Geant4-a simulation toolkit.Nucl.Instrum.Meth.A 506,250-303(2003).https://doi.org/10.1016/S0168-9002(03)01368-8
15.G.Charpak,P.Benaben,P.Breuil,et al.,Performance of wiretype Rn detectors operated with gas gain in ambient air in view of its possible application to early earthquake predictions(2010).arXiv:1002.4732
16.G.Boissonnat,J.M.Fontbonne,J.Colin,et al.,Measurement of ion and electron drift velocity and electronic attachment in air for ionization chambers(2016).arXiv:1609.0374.Accessed 23 Aug2018
17.S.Biagi,Magboltz-Transport of Electrons in Gas Mixtures.CERN Program Library(2000)
18.C.Gao,G.Huang,X.Sun,Topmetal-IIà:a direct charge sensor for high energy physics and imaging applications.J.Instrum.11(1),C01053(2016)
19.T.J.O’Shea,J.Corgan,T.C.Clancy,Convolutional radio modulation recognition networks,in Engineering Applications of Neural Networks,vol.629,EANN 2016.Communications in Computer and Information Science,ed.by C.Jayne,L.Iliadis(Springer,Cham,2016),pp.213-226.https://doi.org/10.1007/978-3-319-44188-7_16
20.Y.Zhu,Q.Ouyang,Y.Mao,A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.BMC Bioinform.18,348(2017).https://doi.org/10.1186/s12859-017-1757-y
21.R.Li,T.Zeng,H.Peng et al.,Deep learning segmentation of optical microscopy images improves 3-D neuron reconstruction.IEEE Trans.Med.Imaging 36,1533-1541(2017).https://doi.org/10.1109/TMI.2017.2679713
22.R.Acciarri,C.Adams,R.An et al.,Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber.J.Instrum.12,P03011(2017)
23.M.An,C.Chen,C.Gao et al.,A low-noise CMOS pixel direct charge sensor Topmetal-IIa for low background and low ratedensity experiments.Nucl.Instrum.Meth.A 810,144-150(2016).https://doi.org/10.1016/j.nima.2015.11.153