Optical and electrical characterization of Cadmium Telluride X-ray pad detectors

Winkler, Alexander; Naaranoja, T.; Gädda, A.; Ott, J.; Luukka, P.; Karadzhinova-Ferrer, A.; Kalliokoski, Matti; Härkönen, J.

doi:10.1016/j.nima.2018.08.032

Cited by 9 publications

(14 citation statements)

References 19 publications

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“…The Ohtu NN [15] used in this study has been explicitly developed for the defect detection in IRM images. Apart from applying it exemplarily on two crystals with low resolution without any postprocessing in [4], it has however not been applied before on new crystal data. The NN has been studied in [16], where it was concluded that it is already sufficiently trained.…”

Section: Neural Network Evaluationmentioning

confidence: 99%

“…9), differences between crystals and layers are clearly visible. Following the categories for high, moderate and low defect density as defined in [4], we can see here Scanning procedure for most of the samples of Batch 2, cf. section 3.3. as well that the first batch (Fig.…”

Section: Comparison Of Different Cdte Crystals and Crystal Batchesmentioning

confidence: 99%

“…Some lines are visible in two or even three layers (Batch 1-01, 1-03, 1-06, 1-08, 1-11, 1-12). As we have three equidistant layers of to compare with the values in [4] we have to multiply these values by a factor of 3 to account for the smaller visibility in depth of 50 𝜇m assumed there Figure 10: 3D defect plots for crystals from Batch 2, dimensions in mm: Batch 1 shows an overall higher defect density than Batch 2 (cf. Fig.…”

Section: Comparison Of Different Cdte Crystals and Crystal Batchesmentioning

confidence: 99%

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Employing infrared microscopy (IRM) in combination with a pre-trained neural network to visualise and analyse the defect distribution in Cadmium Telluride crystals

Kirschenmann,

Bharthuar,

Brücken

et al. 2021

Preprint

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While Cadmium Telluride (CdTe) excels in terms of photon radiation absorption properties and outperforms silicon (Si) in this respect, the crystal growth, characterization and processing into a radiation detector is much more complicated. Additionally, large concentrations of extended crystallographic defects, such as grain boundaries, twins, and tellurium (Te) inclusions, vary from crystal to crystal and can reduce the spectroscopic performance of the processed detector. A quality assessment of the material prior to the complex fabrication process is therefore crucial. To locate the Te-defects, we scan the crystals with infrared microscopy (IRM) in different layers, obtaining a 3D view of the defect distribution. This provides us with important information on the defect density and locations of Te inclusions, and thus a handle to assess the quality of the material. For the classification of defects in the large amount of IRM image data, a convolutional neural network is employed. From the post-processed and analysed IRM data, 3D defect maps of the CdTe crystals are created, which make different patterns of defect agglomerations inside the crystals visible. In total, more than 100 crystals were scanned with the current IRM setup. In this paper, we compare two crystal batches, each consisting of 12 samples. We find significant differences in the defect distributions of the crystals.

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Section: Neural Network Evaluationmentioning

confidence: 99%

Section: Comparison Of Different Cdte Crystals and Crystal Batchesmentioning

confidence: 99%

Section: Comparison Of Different Cdte Crystals and Crystal Batchesmentioning

confidence: 99%

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Employing infrared microscopy (IRM) in combination with a pre-trained neural network to visualise and analyse the defect distribution in Cadmium Telluride crystals

Kirschenmann,

Bharthuar,

Brücken

et al. 2021

Preprint

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“…We use a commercially available infra-red (IR) spectrometer that we modified into a scanning IR imaging device. As CdTe crystals are transparent for IR light, we are able to scan the whole crystal for defects [8]. Tellurium inclusions are not transparent to IR light, thus we can identify those down to sizes of a few µm, close to the diffraction limit.…”

Section: Quality Assurance Of the Raw Materialsmentioning

confidence: 99%

Multispectral photon-counting for medical imaging and beam characterization

et al. 2020

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We present the current status of our project of developing a photon counting detector for medical imaging. An example motivation lays in producing a monitoring and dosimetry device for boron neutron capture therapy, currently not commercially available.Our approach combines in-house developed detectors based on cadmium telluride or thick silicon with readout chip technology developed for particle physics experiments at CERN.Here we describe the manufacturing process of our sensors as well as the processing steps for the assembly of first prototypes. The prototypes use currently the PSI46digV2.1-r readout chip. The accompanying readout electronics chain that was used for first measurements will also be discussed. Finally we present an advanced algorithm developed by us for image reconstruction using such photon counting detectors with focus on boron neutron capture therapy.This work is conducted within a consortium

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“…The basic requirements of the semiconductors for hybrid pixel detectors for X-ray imaging are stability over time and uniformity of crystals properties and detector performance, or in other words, absence of inhomogeneities bigger than the detector spatial resolution [ 13 ]. One of the main causes of detector performance degradation in CdTe and CdZnTe is the presence of Tellurium inclusions, which are known to cause losses in charge collection [ 14 ]. To ensure uniformity in detector panel performance, it is essential to analyze the relation between bulk defects distribution and homogeneity of X-ray response.…”

Section: Introductionmentioning

confidence: 99%

Fabrication of Small-Pixel CdZnTe Sensors and Characterization with X-rays

Tsigaridas

Zanettini²,

Bettelli

et al. 2021

Sensors

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Over the past few years, sensors made from high-Z compound semiconductors have attracted quite some attention for use in applications which require the direct detection of X-rays in the energy range 30–100 keV. One of the candidate materials with promising properties is cadmium zinc telluride (CdZnTe). In the context of this article, we have developed pixelated sensors from CdZnTe crystals grown by Boron oxide encapsulated vertical Bridgman technique. We demonstrate the successful fabrication of CdZnTe pixel sensors with a fine pitch of 55 m and thickness of 1 mm and 2 mm. The sensors were bonded on Timepix readout chips to evaluate their response to X-rays provided by conventional sources. Despite the issues related to single-chip fabrication procedure, reasonable uniformity was achieved along with low leakage current values at room temperature. In addition, the sensors show stable performance over time at moderate incoming fluxes, below 106 photons mm−2s−1.

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Optical and electrical characterization of Cadmium Telluride X-ray pad detectors

Cited by 9 publications

References 19 publications

Employing infrared microscopy (IRM) in combination with a pre-trained neural network to visualise and analyse the defect distribution in Cadmium Telluride crystals

Employing infrared microscopy (IRM) in combination with a pre-trained neural network to visualise and analyse the defect distribution in Cadmium Telluride crystals

Multispectral photon-counting for medical imaging and beam characterization

Fabrication of Small-Pixel CdZnTe Sensors and Characterization with X-rays

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