Hydrogenated amorphous silicon (a-Si:H) can be produced by plasma-enhanced chemical vapor deposition (PECVD) of SiH4 (silane) mixed with hydrogen. The resulting material shows outstanding radiation hardness properties and can be deposited on a wide variety of substrates. Devices employing a-Si:H technologies have been used to detect many different kinds of radiation, namely, minimum ionizing particles (MIPs), X-rays, neutrons, and ions, as well as low-energy protons and alphas. However, the detection of MIPs using planar a-Si:H diodes has proven difficult due to their unsatisfactory S/N ratio arising from a combination of high leakage current, high capacitance, and limited charge collection efficiency (50% at best for a 30 µm planar diode). To overcome these limitations, the 3D-SiAm collaboration proposes employing a 3D detector geometry. The use of vertical electrodes allows for a small collection distance to be maintained while preserving a large detector thickness for charge generation. The depletion voltage in this configuration can be kept below 400 V with a consequent reduction in the leakage current. In this paper, following a detailed description of the fabrication process, the results of the tests performed on the planar p-i-n structures made with ion implantation of the dopants and with carrier selective contacts are illustrated.
Hydrogenated Amorphous Silicon (a-Si:H) is a well known material for its intrinsic radiation hardness and is primarily utilized in solar cells as well as for particle detection and dosimetry. Planar p-i-n diode detectors are fabricated entirely by means of intrinsic and doped PECVD of a mixture of Silane (SiH4) and molecular hydrogen. In order to develop 3D detector geometries using a-Si:H, two options for the junction fabrication have been considered: ion implantation and charge selective contacts through atomic layer deposition. In order to test the functionality of the charge selective contact electrodes, planar detectors have been fabricated utilizing this technique. In this paper, we provide a general overview of the 3D fabrication project followed by the results of leakage current measurements and X-ray dosimetric tests performed on planar diodes containing charge selective contacts to investigate the feasibility of the charge selective contact methodology for integration with the proposed 3D detector architectures.
Background and aim: In modern radiotherapy techniques such as Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) dose gradients are generally high, with variations in space and time of both dose rate and beam energy spectrum. The dosimetry of small fields is challenging due to non-equilibrium conditions, and the introduction of radiation detectors usually perturbs the level of disequilibrium [Das et al., MP 2008]. Thus, the use of detectors for 2D dose verifications able to satisfy dosimetric needs, is mandatory. Polycrystalline Chemical Vapour Deposition (pCVD) Diamond is known to be an attractive material for dosimetric purposes. The aim of the study is the dosimetric characterization of a pCVD diamond bidimensional detector with photon beams produced by the linear accelerator ELEKTA Synergy installed at the Radiotherapy Unit of the University of Florence. Materials and Methods: The detector is composed of two Premium Detector Grade polycrystalline diamond films (Diamond Detector, UK) 2.5×2.5 cm 2 size and 300 μm thick each. In house Cr/Au electric contacts were realized on the front and rear surfaces. The upper contact is in form of a squared matrix of 12×12 pixels: 1.8×1.8mm 2 size, 2 mm pitch; back contact is a square pad. A custom printed circuit board connect each pixel with the electronic read-out board: each pixel is connected to the interface board via semi-rigid silver-silicone pins. For measurement purposes, the two pCVD films were sandwiched inside slabs of water equivalent material (PMMA) 3 cm thick each so as to guarantee the charged particle equilibrium. The detector was fed with 2V while the PCBs, fed with 5 V, ware connected to a PC. The device was placed at the Synergy isocenter and pieces of lead were put on the electronic components to minimize the radiation damage. A pre-irradiation with 5Gy was performed. The signal was acquired in current and charge. Every charge signal was calibrated with corrective factors matrix. Current rise and fall times were evaluated. To test the repeatability, the detector was subject to ten consecutive irradiations each of about 2 Gy, perfomed at three different dose rates: 107, 215 and 430 MU/min (MU, Monitor Unit). The dose rate dependence was studied in the range 52-430 MU/min by fitting the current against the dose rate with the Fowler semi-empirical expression I=I0+RDr (I0 dark current) and by estimating the value. The dose linearity (up to 500MU) was evaluated by fitting the charge signal against the dose for the dose rate 215 and 430 MU/min. The slope of the fit is the detector sensitivity. The Signal Ratios (SRs), defined as the ratio of the detector reading for a specified field size and a reference field, were obtained by varying the field size in the range 0.8×0.8-5.6×5.6 cm 2. The reference field was 3.0×3.0 cm 2. The SRs were compared with the ones measured by a synthetic Single Crystal Diamond Detector (SCDD) developed at the laboratories of "Tor Vergata" University in Rome [Marinelli et al., MP 2015]. SCDD ...
. Purpose The aim of this work is the development and characterization of a model observer (MO) based on convolutional neural networks (CNNs), trained to mimic human observers in image evaluation in terms of detection and localization of low-contrast objects in CT scans acquired on a reference phantom. The final goal is automatic image quality evaluation and CT protocol optimization to fulfill the ALARA principle. Approach Preliminary work was carried out to collect localization confidence ratings of human observers for signal presence/absence from a dataset of 30,000 CT images acquired on a PolyMethyl MethAcrylate phantom containing inserts filled with iodinated contrast media at different concentrations. The collected data were used to generate the labels for the training of the artificial neural networks. We developed and compared two CNN architectures based respectively on Unet and MobileNetV2, specifically adapted to achieve the double tasks of classification and localization. The CNN evaluation was performed by computing the area under localization-ROC curve (LAUC) and accuracy metrics on the test dataset. Results The mean of absolute percentage error between the LAUC of the human observer and MO was found to be below 5% for the most significative test data subsets. An elevated inter-rater agreement was achieved in terms of S-statistics and other common statistical indices. Conclusions Very good agreement was measured between the human observer and MO, as well as between the performance of the two algorithms. Therefore, this work is highly supportive of the feasibility of employing CNN-MO combined with a specifically designed phantom for CT protocol optimization programs.
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