- Email: [email protected]
Radiation damage impact on hybrid-pixel detectors data
Radiation Physics and Chemistry 160 (2019) 63–67
Contents lists available at ScienceDirect
Radiation Physics and Chemistry journal homepage: www.elsevier.com/locate/radphyschem
Radiation damage impact on hybrid-pixel detectors data a,⁎
b
D.P. Magalhaes , J. Rinkel , A. Tomal a b
b
T
Brazilian Synchrotron Light Laboratory - LNLS, Campinas, SP 13083-100, Brazil Gleb Wataghin Physics Institute, State University of Campinas, Campinas, SP 13083-859, Brazil
ARTICLE INFO
ABSTRACT
Keywords: Hybrid pixel detectors X-ray Imaging Radiation damage
This work aimed to quantify the influence of the deposited dose at the hybrid detector ASIC on the resulting image quality. Low (932 ± 4 Gy) and high (6310 ± 24 Gy) dose experiments were performed by irradiating a Medipix3RX single chip detector with the polychromatic beam from the Brazilian Synchrotron X-ray Imaging beamline. It was possible to evaluate subtle effects by using a noise component model based on estimating the quantum, electronic and structural noise contributions. Visible effects were quantified by analyzing the evolution of the histogram of the pixel counts at the irradiated area. The dose threshold for subtle damages was 388 ± 3 Gy deposited in the gate oxide and shallow trench isolation oxide layers, while visible damages were observed for doses higher than 2635 ± 15 Gy. A recovery of the damaged pixels with time was noticed and quantified, reaching the half-life time at 1.84 ± 0.02 h after irradiation. These results encourage periodical maintenance procedures, for example through a new equalization matrix generation, which proved to be a possible tool for recovering the detector performance.
1. Introduction Hybrid pixel detectors are being consolidated as one of the best approaches for X-ray imaging techniques, addressing synchrotron and medical applications (Ballabriga et al., 2016). In this detection technology, the readout circuit is inherently connected behind the sensor and, therefore, X-ray photons transmitted by the sensor can deposit energy at the semiconductor-detector readout ASIC layers. For example, a 300 μm silicon sensor transmits 10% of 10 keV incident photons (Henke et al., 1993). The high intensities of modern synchrotron beams (Lin et al., 2014) and photon energies high enough for reaching the ASIC beneath the sensor (Zhang et al., 2013; Porter et al., 2008) could reduce the detector's life cycle. Even transient errors and recoverable damages can be critical for both synchrotron and medical usages (Porter et al., 2008). In the X-ray energy range from 2 to 100 keV, the damage corresponds to effects at the oxide layers of the CMOS transistors (Zhang et al., 2013; Oldham and McLean, 2003). Basically two different phenomena may occur: electron-hole pairs created by the ionizing photons at the oxide layers can be trapped and alter the transistor switching features, provoking an increase on leakage currents; and, in a further step, the traps can affect the Silicon-to-Oxide interface, producing dangling silicon bonds (Oldham and McLean, 2003). The Medipix3RX detector (Ballabriga et al., 2013) was designed for
⁎
Corresponding author. E-mail address: [email protected] (D.P. Magalhaes).
https://doi.org/10.1016/j.radphyschem.2019.03.011 Received 10 December 2018; Accepted 10 March 2019 Available online 11 March 2019 0969-806X/ © 2019 Elsevier Ltd. All rights reserved.
medical applications and stands out for being a 0.13 μm CMOS technology ASIC. The use of this technology enables the pixel size to be 55 μm square, still contemplating a complex signal processing embedded in each pixel. Its analog circuitry was designed with radiation hardness techniques (Ballabriga, 2009), in order to prevent damages in this processing level. The digital circuitry, however, is much more sensitive to radiation effects. Also, this ASIC, as most 0.13 μm CMOS devices, was designed with a dielectric isolation between adjacent transistors. The trapped charges at this layer have been reported to also impact these CMOS devices performance (Barnaby et al., 2007). This work aimed to quantify the radiation effect on the photon counting image obtained with the detector, and relating it to the deposited dose at the ASIC front-end oxide layers. The deposited dose was estimated based on Monte Carlo simulation, while the damage was evaluated by gradually exposing a Medipix3RX single chip to a synchrotron bending magnet beam and analyzing the acquired image from the detector. 2. Materials and methods 2.1. Experimental Setup A Medipix3RX single chip bump-bonded to a silicon sensor 200 μm thick was used in this study. The experimental measurements were
Radiation Physics and Chemistry 160 (2019) 63–67
D.P. Magalhaes, et al.
performed at the Brazilian Synchrotron X-ray Imaging Beamline, due to its easy and fast procedure of removing the monochromator from the beam path, allowing direct access to the bending magnet white beam. The experiment was divided in two sets: a preliminary set, aiming at observing subtle effects generated by low doses, and a further set for high dose rates. The dose rate was selected by adding silicon filters in front of the beam. Both experiment sets were performed as a sequence of steps, each one as follows: a selected area of the detector was irradiated with white beam during a measured time; the beamline was set to 8 keV monochromatic mode, irradiating the same area; images were taken with the detector in Single Pixel Mode, 12 bits mode (Ballabriga et al., 2013), with different acquisition times, for its response characterization; the cycle was repeated for the next step. In the low dose set of measurements, 3 mm of silicon were used as filtration in the white beam irradiation phase. In the high dose set, 1.25 mm of silicon were used. Different areas of the detector were irradiated in the sets, so each effect could be analyzed separately.
13 ± 1 Gy/minute for the first 9 steps, changing to 4.7 ± 0.5 Gy/minute for the next 9 steps, and ending at 5.2 ± 0.1 Gy/minute for the last 20 steps, taking breaks of 96.7 min and 224.4 min between the different dose rate measurements. In the second high dose region, the dose rate was maintained in average 5.91 ± 0.07 Gy/minute throughout the shift, without significant time breaks. 2.4. Data analysis The analysis was performed considering the whole irradiated areas of 3 × 6 mm, each one corresponding to 4992 pixels. A noise component model used for CCD imaging detectors (Evans et al., 2002) was adapted for the analysis of the subtle radiation damage influence on the photon counting image data. The model comprehends three noise sources: the electronic, the quantum and the structural noises. These components can be discriminated based on their evolution with the average number of counts in the irradiated pixels ( µ ), as described by Eq. (1):
2.2. Dose simulation
µ
The experiment was simulated in the PENELOPE Monte Carlo Simulation tool (Salvat, 2015), to estimate the deposited dose at the oxide layers of the ASIC as a function of the incident fluence (Magalhaes and Tomal, 2018). The gate oxide and the Shallow trench isolation layers were considered. The energy spectrum generated by the bending magnet was obtained analytically through the software XOP (Del Rio and Dejus, 1997), considering the synchrotron ring and the beamline bending magnet specific features. The attenuation due to elements in the beam trajectory, such as beryllium windows, air gaps and the selected filters, was calculated analytically, based on the Center of X-ray Optics database for transmission data (Henke et al., 1993), in order to spare simulation time. The beam intensity distribution in the counting area and the photons loss due to slits arrangement were also accounted.
(µ ) =
kq2 ke2 + + ks2 2 µ µ
(1)
For hybrid pixel detectors, the electronic noise component ke is expected to be near zero, once it is cut by the energy threshold (Ballabriga, 2009; Evans et al., 2002). The quantum noise contribution is described by the Poisson statistics, so the component kq should be closer or equal to 1, being independent on the detector (Knoll, 2000). Therefore, the damage effect is expected to manifest in the structural noise component ks , which refers to pixel-to-pixel variations inherent to the detector. In order to evaluate the visible detector damages in the image, the counting histograms of the images were studied to identify the distribution of pixels presenting distinct behaviors in the high dose experiment. The evolution of the percentage of the pixels groups with dose was also analyzed as a damage metric. This analysis was applied to the high dose rate data, since no visible damage was observed in the low dose rate images.
2.3. Experimental procedure The low dose set consisted of 30 steps of variable exposure times, reaching 932 ± 4 Gy deposited at the front-end oxide layers. The steps were divided in two beam shifts, separated by a 700 min recovery break. The first shift included 12 steps in a dose rate of 0.61 ± 0.04 Gy/minute, and the second included the last 17 steps with a 0.66 ± 0.03 Gy/minute rate. The high dose set was reproduced in two irradiated pixel regions. The first reached 6260 ± 37 Gy in 40 dose steps; and the second, 6310 ± 24 Gy in 51 steps. The dose rate varied in the first region, beginning with
3. Results and discussion 3.1. Damage analysis 3.1.1. Noise components Fig. 1a exhibits the relative noise curve of an intermediate dose step used for the noise components analysis, based on two approaches:
Fig. 1. (a) Example of the normalized standard deviation curve obtained for an intermediate irradiation step, considering only the healthy pixels (Histogram Gaussian method) and of the whole image, disregarding only the dead and saturated pixels. The difference is explained by the blemished pixels with altered gain. (b) Example of the Gaussian approach at the histogram for two dose steps. 64
Radiation Physics and Chemistry 160 (2019) 63–67
D.P. Magalhaes, et al.
Fig. 2. (a) ks parameter as a function of the deposited dose, for the irradiated positions; and (b) ks for the low dose rate experiment, in detail.
Fig. 3. Image taken of one of the high dose irradiated areas, (a) before and (b) after the experiment; (c) Histograms of the acquisitions obtained for each irradiation step of the same experiment, highlighting the counting groups.
taking the full image relative standard deviation as a function of the average number of counts; or fitting the image histogram to a Gaussian function and analyzing the Gaussian relative standard deviation as a function of the average. The dead and saturated pixels are withdrawn for the relative noise calculation in both approaches. Fig. 1b shows an example of the Gaussian approach for two different dose steps, basing on the image histogram for high average counts. Fig. 1a illustrates that the relative noise determined based on the Gaussian approach presents the expected decrease with the average counts, being able to be fitted by Eq. (1), while the whole image noise approach includes pixels with altered gain, orblemishes (Ponchut, 2005). For higher average counts, the blemishes counters saturate and are withdrawn from the relative noise calculation; the two approaches then converge to the same asymptotic value, where the most relevant component is the structural noise parameter ks , as can be inferred from
Eq. (1). To reduce the data process computational cost, theks parameter was obtained by the analysis of the relative noise for high acquisition time images. Fig. 2a exhibits the ks parameter values obtained for the three experiment sets: the low dose rate set and the two high dose rate sets. Fig. 2b details the low dose rate result, to highlight the dose threshold of 388 ± 3 Gy in the front-end oxide layers, from which the ks parameter started to grow linearly beyond the margin of error. For comparison, it was estimated that this dose threshold corresponds to a Total Ionizing Dose (TID) of (2.4 ± 0.2) × 103 Gy deposited in the whole detector system, considering sensor and ASIC layers. Similar ks growing rates were obtained for the two high dose regions: units/Gy and (1.03 ± 0.02) × 10 5 units/Gy for high dose positions I and II, respectively. This evidences that the noise response does not depend greatly of the dose rate. A higher growing rate of
65
Radiation Physics and Chemistry 160 (2019) 63–67
D.P. Magalhaes, et al.
Fig. 6. Evaluation of the number of G5 pixels in the irradiated area as a function of the recovery time since the end of the irradiation, and it's exponential fit.
Fig. 4. Percentage of pixels for each group and its evolution with dose. A dose threshold can be inferred from which the healthy pixels group start diminishing.
The percentage of each group as a function of the deposited dose is presented in Fig. 4. It can be observed that the percentage of healthy pixels is almost constant for doses smaller than 2635 ± 15 Gy. This value can be stated as a high dose threshold for visible radiation effects to the image, and corresponds to a Total Ionizing Dose of (2.5 ± 0.2) × 10 4 Gy deposited in the entire detector system. The second region irradiated corroborated this behavior, providing a dose threshold of 2666 ± 30 Gy. For doses above this threshold, the percentage of the other groups increase due to the increasing of count fails with dose. 3.2. Detector recovery 3.2.1. Break periods Fig. 5 exhibits the percentage of healthy pixels for the two high dose regions as a function of the simulated dose, compared with the time breaks before the measurements. A correlation can be noticed between improvements in the healthy pixels percentage and the time breaks of irradiation between dose steps. The number of saturated pixels (G5) at the first highly irradiated position was also measured after the experiment. Fig. 6 shows an exponential decay of the number of saturated pixels with time, with a half-life time of 1.84 ± 0.02 h. However, a total pixel recovery was not achieved, since a week after the experiment this region remained with 6 hot pixels.
Fig. 5. Healthy pixels percentage time evolution, comparing to the recovery break time before each step. Higher recovery times are associated with improvements on the healthy pixels amount.
(1.7 ± 0.1) × 10 5 units/Gy was obtained for the low dose rate above the threshold of 338 ± 3 Gy. The differences in intercept values can be associated to structural differences between the pixel regions, since a different relative noise is observed for zero dose, attributed to the pixelto-pixel mismatch inherent of the detector.
3.2.2. Equalization In order to investigate if a pixel-to-pixel mismatch equalization procedure (Rinkel et al., 2015) would recover the radiation damage impacts, a comparison of the relative noise at the exposed regions with the previous equalization and with a new equalization was made. Fig. 7a exhibits an image obtained with a Siemens K710-H X-ray generator after the experiment, where the irradiated areas can be visually identified by their extra noise. Fig. 7b exhibits the difference between the equalization matrix generated previously for this detector and a new equalization, generated after the experiment. The damaged areas present a higher difference, which indicates the equalization procedure effort on recovering these pixels. The calculated improvement on the ks parameter with the new equalization has reached 8.7% and 5.6% for the two high dose positions. The low dose position did not present a quantifiable improvement, which can be a consequence of this region time recovery.
3.1.2. Pixel groups In the high dose rate experiment, it was possible to identify five different counting behaviors of the pixels based on the histograms of counts in the image. Fig. 3a exhibits one of the irradiated regions before and 3b after the experiment, and Fig. 3c the histograms of each dose step for this region. According to Fig. 3c, five different regions can be identified on the histogram. The pixels counting zero (dead pixels) were gathered at group G1; the healthy pixels presenting a Gaussian distribution were gathered at group G2; pixels counting 2159, corresponding to the columns observed in Fig. 3b, were gathered in group G3; a group of pixels with altered gain, counting around 3 times the average counts of the healthy pixels, were grouped in G4; and the saturated pixels were grouped in G5.
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Fig. 7. (a) X-ray image obtained with the detector after the experiment; (b) Difference between the equalization matrix obtained after the experiment and the one obtained before the experiment, highlighting the irradiated areas.
4. Conclusions
Ballabriga, R., 2009. The Design and Implementation in 0.13 µm CMOS of an Algorithm Per-mitting Spectroscopic Imaging with High Spatial Resolution for Hybrid Pixel Detectors (Ph.D. thesis). Universitat Ramon Llull, Barcelona, Spain (CERN-THESIS2010-055). Barnaby, H., et al., 2007. Total-ionizing-dose effects on isolation oxides in modern CMOS technologies. Nucl. Instrum. Methods Phys. Res. B 261, 1142–1145. https://doi.org/ 10.1016/j.nimb.2007.03.109. Del Rio, M. S. and Dejus, R. J. (1997). XOP: A multiplatform graphical user interface for syn-chrotron radiation spectral and optics calculations. Proc. SPIE 3152, Materials, Manufacturing, andMeasurement for Synchrotron Radiation Mirrors. 〈https://doi. org/10.1117/12.295554〉. Evans, D.S., et al., 2002. A comparison of the imaging properties of CCD-based devices used for small field digital mammography. Phys. Med. Biol. 47, 117(〈http://www. stacks.iop.org/PMB/47/117〉). Henke, B.L., et al., 1993a. X-ray interactions - photoabsorption, scattering, transmission and reflection at E=50-30,000 eV,Z = 1-92. At. Data Nucl. Data Tables 54, 181–342. https://doi.org/10.1006/adnd.1993.1013. (No 2). Knoll, G., 2000. Radiation Detection and Measurement, 3rd ed. John Wiley & Sons, New York (Chapter 4). Lin, L., et al., 2014. The Sirius project. J. Synchrotron Radiat. 21, 904–911. https://doi. org/10.1107/S1600577514011928. Magalhaes, D., Tomal, A., 2018. Monte Carlo simulation of hybrid pixel detectors. Radiat. Chem. Phys (submitted for publication). Oldham, T., McLean, F., 2003. Total Ionizing Dose effects in MOS oxides and devices. IEEE Trans. Nucl. Sci. 50, 483–499. https://doi.org/10.1109/TNS.2003.812927. Ponchut, C., 2005. Characterization of X-ray area detectors for synchrotron beamlines. J. Synchrotron Radiat. 13, 195–203. https://doi.org/10.1107/S0909049505034278. Porter, M., et al. (2008). Soft error reliability improvements for implantable medical devices. In: Proceedings of the International Reliability Physics Symposium. 〈https:// doi.org/10.1109/RELPHY.2008.4558934〉. Rinkel, J., et al., 2015. Equalization method for Medipix3RX. Nucl. Instrum. Methods Phys. Res. A 801, 1–6. https://doi.org/10.1016/j.nima.2015.08.029. Salvat, F., 2015. PENELOPE-2014: A Code System for Monte Carlo Simulation of Electron and Photon Transport. NEA Data Bank, Barcelona, Spain(〈https://www.oecd-nea. org/science/docs/2015/nsc-doc2015-3.pdf〉). Zhang, J., et al., 2013. Investigation of X-ray induced radiation damage at the Si-SiO2 interface of silicon sensors for the European XFEL. J. Instrum. 7, C12012. https://doi. org/10.1088/1748-0221/7/12/C12012.
It was possible to quantify the X-ray radiation dose effect on a hybrid-pixel detector photon counting image, correlating to the deposited dose on the oxide layers of the ASIC active region. Dose thresholds were obtained for both subtle and visible radiation effects. Based on this knowledge, estimations of lifetime can be obtained for the detector according to the application and sensor characteristics. Also, the influence on the detector recovery by time and by a new equalization procedure were quantified. For synchrotron applications, this result can lead to estimations on the periodical maintenance procedure frequency according to the detector's usage. Acknowledgements The authors gratefully acknowledge the Ministry of Science, Technology, Innovation and Communication and the Brazilian Agency Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP, Brazil (Processes 2015/21873-8 and 2011/51594-2) for financial support. The authors also thanks the Brazilian Synchrotron Light Laboratory (LNLS), specially the Detectors Group and the X-ray Imaging Beamline staff for technical support. References Ballabriga, R., et al., 2013. The Medipix3RX: a high-resolution, zero dead-time pixel detector readout chip allowing spectroscopic imaging. J. Instrum. 8, C02016. https:// doi.org/10.1088/1748-0221/8/02/C02016. Ballabriga, R., et al., 2016. Review of hybrid pixel detector readout ASICs for spectroscopic X-ray imaging. J. Instrum. 11, P01007. https://doi.org/10.1088/1748-0221/ 11/01/P01007.
67
Contents lists available at ScienceDirect
Radiation Physics and Chemistry journal homepage: www.elsevier.com/locate/radphyschem
Radiation damage impact on hybrid-pixel detectors data a,⁎
b
D.P. Magalhaes , J. Rinkel , A. Tomal a b
b
T
Brazilian Synchrotron Light Laboratory - LNLS, Campinas, SP 13083-100, Brazil Gleb Wataghin Physics Institute, State University of Campinas, Campinas, SP 13083-859, Brazil
ARTICLE INFO
ABSTRACT
Keywords: Hybrid pixel detectors X-ray Imaging Radiation damage
This work aimed to quantify the influence of the deposited dose at the hybrid detector ASIC on the resulting image quality. Low (932 ± 4 Gy) and high (6310 ± 24 Gy) dose experiments were performed by irradiating a Medipix3RX single chip detector with the polychromatic beam from the Brazilian Synchrotron X-ray Imaging beamline. It was possible to evaluate subtle effects by using a noise component model based on estimating the quantum, electronic and structural noise contributions. Visible effects were quantified by analyzing the evolution of the histogram of the pixel counts at the irradiated area. The dose threshold for subtle damages was 388 ± 3 Gy deposited in the gate oxide and shallow trench isolation oxide layers, while visible damages were observed for doses higher than 2635 ± 15 Gy. A recovery of the damaged pixels with time was noticed and quantified, reaching the half-life time at 1.84 ± 0.02 h after irradiation. These results encourage periodical maintenance procedures, for example through a new equalization matrix generation, which proved to be a possible tool for recovering the detector performance.
1. Introduction Hybrid pixel detectors are being consolidated as one of the best approaches for X-ray imaging techniques, addressing synchrotron and medical applications (Ballabriga et al., 2016). In this detection technology, the readout circuit is inherently connected behind the sensor and, therefore, X-ray photons transmitted by the sensor can deposit energy at the semiconductor-detector readout ASIC layers. For example, a 300 μm silicon sensor transmits 10% of 10 keV incident photons (Henke et al., 1993). The high intensities of modern synchrotron beams (Lin et al., 2014) and photon energies high enough for reaching the ASIC beneath the sensor (Zhang et al., 2013; Porter et al., 2008) could reduce the detector's life cycle. Even transient errors and recoverable damages can be critical for both synchrotron and medical usages (Porter et al., 2008). In the X-ray energy range from 2 to 100 keV, the damage corresponds to effects at the oxide layers of the CMOS transistors (Zhang et al., 2013; Oldham and McLean, 2003). Basically two different phenomena may occur: electron-hole pairs created by the ionizing photons at the oxide layers can be trapped and alter the transistor switching features, provoking an increase on leakage currents; and, in a further step, the traps can affect the Silicon-to-Oxide interface, producing dangling silicon bonds (Oldham and McLean, 2003). The Medipix3RX detector (Ballabriga et al., 2013) was designed for
⁎
Corresponding author. E-mail address: [email protected] (D.P. Magalhaes).
https://doi.org/10.1016/j.radphyschem.2019.03.011 Received 10 December 2018; Accepted 10 March 2019 Available online 11 March 2019 0969-806X/ © 2019 Elsevier Ltd. All rights reserved.
medical applications and stands out for being a 0.13 μm CMOS technology ASIC. The use of this technology enables the pixel size to be 55 μm square, still contemplating a complex signal processing embedded in each pixel. Its analog circuitry was designed with radiation hardness techniques (Ballabriga, 2009), in order to prevent damages in this processing level. The digital circuitry, however, is much more sensitive to radiation effects. Also, this ASIC, as most 0.13 μm CMOS devices, was designed with a dielectric isolation between adjacent transistors. The trapped charges at this layer have been reported to also impact these CMOS devices performance (Barnaby et al., 2007). This work aimed to quantify the radiation effect on the photon counting image obtained with the detector, and relating it to the deposited dose at the ASIC front-end oxide layers. The deposited dose was estimated based on Monte Carlo simulation, while the damage was evaluated by gradually exposing a Medipix3RX single chip to a synchrotron bending magnet beam and analyzing the acquired image from the detector. 2. Materials and methods 2.1. Experimental Setup A Medipix3RX single chip bump-bonded to a silicon sensor 200 μm thick was used in this study. The experimental measurements were
Radiation Physics and Chemistry 160 (2019) 63–67
D.P. Magalhaes, et al.
performed at the Brazilian Synchrotron X-ray Imaging Beamline, due to its easy and fast procedure of removing the monochromator from the beam path, allowing direct access to the bending magnet white beam. The experiment was divided in two sets: a preliminary set, aiming at observing subtle effects generated by low doses, and a further set for high dose rates. The dose rate was selected by adding silicon filters in front of the beam. Both experiment sets were performed as a sequence of steps, each one as follows: a selected area of the detector was irradiated with white beam during a measured time; the beamline was set to 8 keV monochromatic mode, irradiating the same area; images were taken with the detector in Single Pixel Mode, 12 bits mode (Ballabriga et al., 2013), with different acquisition times, for its response characterization; the cycle was repeated for the next step. In the low dose set of measurements, 3 mm of silicon were used as filtration in the white beam irradiation phase. In the high dose set, 1.25 mm of silicon were used. Different areas of the detector were irradiated in the sets, so each effect could be analyzed separately.
13 ± 1 Gy/minute for the first 9 steps, changing to 4.7 ± 0.5 Gy/minute for the next 9 steps, and ending at 5.2 ± 0.1 Gy/minute for the last 20 steps, taking breaks of 96.7 min and 224.4 min between the different dose rate measurements. In the second high dose region, the dose rate was maintained in average 5.91 ± 0.07 Gy/minute throughout the shift, without significant time breaks. 2.4. Data analysis The analysis was performed considering the whole irradiated areas of 3 × 6 mm, each one corresponding to 4992 pixels. A noise component model used for CCD imaging detectors (Evans et al., 2002) was adapted for the analysis of the subtle radiation damage influence on the photon counting image data. The model comprehends three noise sources: the electronic, the quantum and the structural noises. These components can be discriminated based on their evolution with the average number of counts in the irradiated pixels ( µ ), as described by Eq. (1):
2.2. Dose simulation
µ
The experiment was simulated in the PENELOPE Monte Carlo Simulation tool (Salvat, 2015), to estimate the deposited dose at the oxide layers of the ASIC as a function of the incident fluence (Magalhaes and Tomal, 2018). The gate oxide and the Shallow trench isolation layers were considered. The energy spectrum generated by the bending magnet was obtained analytically through the software XOP (Del Rio and Dejus, 1997), considering the synchrotron ring and the beamline bending magnet specific features. The attenuation due to elements in the beam trajectory, such as beryllium windows, air gaps and the selected filters, was calculated analytically, based on the Center of X-ray Optics database for transmission data (Henke et al., 1993), in order to spare simulation time. The beam intensity distribution in the counting area and the photons loss due to slits arrangement were also accounted.
(µ ) =
kq2 ke2 + + ks2 2 µ µ
(1)
For hybrid pixel detectors, the electronic noise component ke is expected to be near zero, once it is cut by the energy threshold (Ballabriga, 2009; Evans et al., 2002). The quantum noise contribution is described by the Poisson statistics, so the component kq should be closer or equal to 1, being independent on the detector (Knoll, 2000). Therefore, the damage effect is expected to manifest in the structural noise component ks , which refers to pixel-to-pixel variations inherent to the detector. In order to evaluate the visible detector damages in the image, the counting histograms of the images were studied to identify the distribution of pixels presenting distinct behaviors in the high dose experiment. The evolution of the percentage of the pixels groups with dose was also analyzed as a damage metric. This analysis was applied to the high dose rate data, since no visible damage was observed in the low dose rate images.
2.3. Experimental procedure The low dose set consisted of 30 steps of variable exposure times, reaching 932 ± 4 Gy deposited at the front-end oxide layers. The steps were divided in two beam shifts, separated by a 700 min recovery break. The first shift included 12 steps in a dose rate of 0.61 ± 0.04 Gy/minute, and the second included the last 17 steps with a 0.66 ± 0.03 Gy/minute rate. The high dose set was reproduced in two irradiated pixel regions. The first reached 6260 ± 37 Gy in 40 dose steps; and the second, 6310 ± 24 Gy in 51 steps. The dose rate varied in the first region, beginning with
3. Results and discussion 3.1. Damage analysis 3.1.1. Noise components Fig. 1a exhibits the relative noise curve of an intermediate dose step used for the noise components analysis, based on two approaches:
Fig. 1. (a) Example of the normalized standard deviation curve obtained for an intermediate irradiation step, considering only the healthy pixels (Histogram Gaussian method) and of the whole image, disregarding only the dead and saturated pixels. The difference is explained by the blemished pixels with altered gain. (b) Example of the Gaussian approach at the histogram for two dose steps. 64
Radiation Physics and Chemistry 160 (2019) 63–67
D.P. Magalhaes, et al.
Fig. 2. (a) ks parameter as a function of the deposited dose, for the irradiated positions; and (b) ks for the low dose rate experiment, in detail.
Fig. 3. Image taken of one of the high dose irradiated areas, (a) before and (b) after the experiment; (c) Histograms of the acquisitions obtained for each irradiation step of the same experiment, highlighting the counting groups.
taking the full image relative standard deviation as a function of the average number of counts; or fitting the image histogram to a Gaussian function and analyzing the Gaussian relative standard deviation as a function of the average. The dead and saturated pixels are withdrawn for the relative noise calculation in both approaches. Fig. 1b shows an example of the Gaussian approach for two different dose steps, basing on the image histogram for high average counts. Fig. 1a illustrates that the relative noise determined based on the Gaussian approach presents the expected decrease with the average counts, being able to be fitted by Eq. (1), while the whole image noise approach includes pixels with altered gain, orblemishes (Ponchut, 2005). For higher average counts, the blemishes counters saturate and are withdrawn from the relative noise calculation; the two approaches then converge to the same asymptotic value, where the most relevant component is the structural noise parameter ks , as can be inferred from
Eq. (1). To reduce the data process computational cost, theks parameter was obtained by the analysis of the relative noise for high acquisition time images. Fig. 2a exhibits the ks parameter values obtained for the three experiment sets: the low dose rate set and the two high dose rate sets. Fig. 2b details the low dose rate result, to highlight the dose threshold of 388 ± 3 Gy in the front-end oxide layers, from which the ks parameter started to grow linearly beyond the margin of error. For comparison, it was estimated that this dose threshold corresponds to a Total Ionizing Dose (TID) of (2.4 ± 0.2) × 103 Gy deposited in the whole detector system, considering sensor and ASIC layers. Similar ks growing rates were obtained for the two high dose regions: units/Gy and (1.03 ± 0.02) × 10 5 units/Gy for high dose positions I and II, respectively. This evidences that the noise response does not depend greatly of the dose rate. A higher growing rate of
65
Radiation Physics and Chemistry 160 (2019) 63–67
D.P. Magalhaes, et al.
Fig. 6. Evaluation of the number of G5 pixels in the irradiated area as a function of the recovery time since the end of the irradiation, and it's exponential fit.
Fig. 4. Percentage of pixels for each group and its evolution with dose. A dose threshold can be inferred from which the healthy pixels group start diminishing.
The percentage of each group as a function of the deposited dose is presented in Fig. 4. It can be observed that the percentage of healthy pixels is almost constant for doses smaller than 2635 ± 15 Gy. This value can be stated as a high dose threshold for visible radiation effects to the image, and corresponds to a Total Ionizing Dose of (2.5 ± 0.2) × 10 4 Gy deposited in the entire detector system. The second region irradiated corroborated this behavior, providing a dose threshold of 2666 ± 30 Gy. For doses above this threshold, the percentage of the other groups increase due to the increasing of count fails with dose. 3.2. Detector recovery 3.2.1. Break periods Fig. 5 exhibits the percentage of healthy pixels for the two high dose regions as a function of the simulated dose, compared with the time breaks before the measurements. A correlation can be noticed between improvements in the healthy pixels percentage and the time breaks of irradiation between dose steps. The number of saturated pixels (G5) at the first highly irradiated position was also measured after the experiment. Fig. 6 shows an exponential decay of the number of saturated pixels with time, with a half-life time of 1.84 ± 0.02 h. However, a total pixel recovery was not achieved, since a week after the experiment this region remained with 6 hot pixels.
Fig. 5. Healthy pixels percentage time evolution, comparing to the recovery break time before each step. Higher recovery times are associated with improvements on the healthy pixels amount.
(1.7 ± 0.1) × 10 5 units/Gy was obtained for the low dose rate above the threshold of 338 ± 3 Gy. The differences in intercept values can be associated to structural differences between the pixel regions, since a different relative noise is observed for zero dose, attributed to the pixelto-pixel mismatch inherent of the detector.
3.2.2. Equalization In order to investigate if a pixel-to-pixel mismatch equalization procedure (Rinkel et al., 2015) would recover the radiation damage impacts, a comparison of the relative noise at the exposed regions with the previous equalization and with a new equalization was made. Fig. 7a exhibits an image obtained with a Siemens K710-H X-ray generator after the experiment, where the irradiated areas can be visually identified by their extra noise. Fig. 7b exhibits the difference between the equalization matrix generated previously for this detector and a new equalization, generated after the experiment. The damaged areas present a higher difference, which indicates the equalization procedure effort on recovering these pixels. The calculated improvement on the ks parameter with the new equalization has reached 8.7% and 5.6% for the two high dose positions. The low dose position did not present a quantifiable improvement, which can be a consequence of this region time recovery.
3.1.2. Pixel groups In the high dose rate experiment, it was possible to identify five different counting behaviors of the pixels based on the histograms of counts in the image. Fig. 3a exhibits one of the irradiated regions before and 3b after the experiment, and Fig. 3c the histograms of each dose step for this region. According to Fig. 3c, five different regions can be identified on the histogram. The pixels counting zero (dead pixels) were gathered at group G1; the healthy pixels presenting a Gaussian distribution were gathered at group G2; pixels counting 2159, corresponding to the columns observed in Fig. 3b, were gathered in group G3; a group of pixels with altered gain, counting around 3 times the average counts of the healthy pixels, were grouped in G4; and the saturated pixels were grouped in G5.
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Fig. 7. (a) X-ray image obtained with the detector after the experiment; (b) Difference between the equalization matrix obtained after the experiment and the one obtained before the experiment, highlighting the irradiated areas.
4. Conclusions
Ballabriga, R., 2009. The Design and Implementation in 0.13 µm CMOS of an Algorithm Per-mitting Spectroscopic Imaging with High Spatial Resolution for Hybrid Pixel Detectors (Ph.D. thesis). Universitat Ramon Llull, Barcelona, Spain (CERN-THESIS2010-055). Barnaby, H., et al., 2007. Total-ionizing-dose effects on isolation oxides in modern CMOS technologies. Nucl. Instrum. Methods Phys. Res. B 261, 1142–1145. https://doi.org/ 10.1016/j.nimb.2007.03.109. Del Rio, M. S. and Dejus, R. J. (1997). XOP: A multiplatform graphical user interface for syn-chrotron radiation spectral and optics calculations. Proc. SPIE 3152, Materials, Manufacturing, andMeasurement for Synchrotron Radiation Mirrors. 〈https://doi. org/10.1117/12.295554〉. Evans, D.S., et al., 2002. A comparison of the imaging properties of CCD-based devices used for small field digital mammography. Phys. Med. Biol. 47, 117(〈http://www. stacks.iop.org/PMB/47/117〉). Henke, B.L., et al., 1993a. X-ray interactions - photoabsorption, scattering, transmission and reflection at E=50-30,000 eV,Z = 1-92. At. Data Nucl. Data Tables 54, 181–342. https://doi.org/10.1006/adnd.1993.1013. (No 2). Knoll, G., 2000. Radiation Detection and Measurement, 3rd ed. John Wiley & Sons, New York (Chapter 4). Lin, L., et al., 2014. The Sirius project. J. Synchrotron Radiat. 21, 904–911. https://doi. org/10.1107/S1600577514011928. Magalhaes, D., Tomal, A., 2018. Monte Carlo simulation of hybrid pixel detectors. Radiat. Chem. Phys (submitted for publication). Oldham, T., McLean, F., 2003. Total Ionizing Dose effects in MOS oxides and devices. IEEE Trans. Nucl. Sci. 50, 483–499. https://doi.org/10.1109/TNS.2003.812927. Ponchut, C., 2005. Characterization of X-ray area detectors for synchrotron beamlines. J. Synchrotron Radiat. 13, 195–203. https://doi.org/10.1107/S0909049505034278. Porter, M., et al. (2008). Soft error reliability improvements for implantable medical devices. In: Proceedings of the International Reliability Physics Symposium. 〈https:// doi.org/10.1109/RELPHY.2008.4558934〉. Rinkel, J., et al., 2015. Equalization method for Medipix3RX. Nucl. Instrum. Methods Phys. Res. A 801, 1–6. https://doi.org/10.1016/j.nima.2015.08.029. Salvat, F., 2015. PENELOPE-2014: A Code System for Monte Carlo Simulation of Electron and Photon Transport. NEA Data Bank, Barcelona, Spain(〈https://www.oecd-nea. org/science/docs/2015/nsc-doc2015-3.pdf〉). Zhang, J., et al., 2013. Investigation of X-ray induced radiation damage at the Si-SiO2 interface of silicon sensors for the European XFEL. J. Instrum. 7, C12012. https://doi. org/10.1088/1748-0221/7/12/C12012.
It was possible to quantify the X-ray radiation dose effect on a hybrid-pixel detector photon counting image, correlating to the deposited dose on the oxide layers of the ASIC active region. Dose thresholds were obtained for both subtle and visible radiation effects. Based on this knowledge, estimations of lifetime can be obtained for the detector according to the application and sensor characteristics. Also, the influence on the detector recovery by time and by a new equalization procedure were quantified. For synchrotron applications, this result can lead to estimations on the periodical maintenance procedure frequency according to the detector's usage. Acknowledgements The authors gratefully acknowledge the Ministry of Science, Technology, Innovation and Communication and the Brazilian Agency Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP, Brazil (Processes 2015/21873-8 and 2011/51594-2) for financial support. The authors also thanks the Brazilian Synchrotron Light Laboratory (LNLS), specially the Detectors Group and the X-ray Imaging Beamline staff for technical support. References Ballabriga, R., et al., 2013. The Medipix3RX: a high-resolution, zero dead-time pixel detector readout chip allowing spectroscopic imaging. J. Instrum. 8, C02016. https:// doi.org/10.1088/1748-0221/8/02/C02016. Ballabriga, R., et al., 2016. Review of hybrid pixel detector readout ASICs for spectroscopic X-ray imaging. J. Instrum. 11, P01007. https://doi.org/10.1088/1748-0221/ 11/01/P01007.
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