An Array of CdTe Detectors for Imaging Applications

1993 ◽  
Vol 302 ◽  
Author(s):  
Y. Eisen ◽  
E. Polak

ABSTRACTThe performance characteristics of a linear array composed of several hundred miniature spectroscopy grade CdTe detectors are described. The linear array is utilized in a X-ray security imaging system. It operates either in an energy dispersive mode to highlight certain atomic elements in the luggage or in a regular mode for obtaining only the transmission image of the luggage. The array functions in the photon counting mode and tolerates count rates as high as 106 counts/sec. The array has high quantum efficiency and good spatial resolution which result in excellent image quality. The dynamic range, the electronic noise, the energy resolution, the homogeneity of response among detectors and the image quality are presented.

1994 ◽  
Vol 299 ◽  
Author(s):  
Y. Eisen ◽  
E. Polak

AbstractThe performance characteristics of a linear array composed of several hundred miniature spectroscopy grade CdTe detectors are described. The linear array is utilized in a X-ray security imaging system. It operates either in an energy dispersive mode to highlight certain atomic elements in the luggage or in a regular mode for obtaining only the transmission image of the luggage. The array functions in the photon counting mode and tolerates count rates as high as 106 counts/sec. The array has high quantum efficiency and good spatial resolution which result in excellent image quality. The dynamic range, the electronic noise, the energy resolution, the homogeneity of response among detectors and the image quality are presented.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ibtissame Khaoua ◽  
Guillaume Graciani ◽  
Andrey Kim ◽  
François Amblard

AbstractFor a wide range of purposes, one faces the challenge to detect light from extremely faint and spatially extended sources. In such cases, detector noises dominate over the photon noise of the source, and quantum detectors in photon counting mode are generally the best option. Here, we combine a statistical model with an in-depth analysis of detector noises and calibration experiments, and we show that visible light can be detected with an electron-multiplying charge-coupled devices (EM-CCD) with a signal-to-noise ratio (SNR) of 3 for fluxes less than $$30\,{\text{photon}}\,{\text{s}}^{ - 1} \,{\text{cm}}^{ - 2}$$ 30 photon s - 1 cm - 2 . For green photons, this corresponds to 12 aW $${\text{cm}}^{ - 2}$$ cm - 2 ≈ $$9{ } \times 10^{ - 11}$$ 9 × 10 - 11 lux, i.e. 15 orders of magnitude less than typical daylight. The strong nonlinearity of the SNR with the sampling time leads to a dynamic range of detection of 4 orders of magnitude. To detect possibly varying light fluxes, we operate in conditions of maximal detectivity $${\mathcal{D}}$$ D rather than maximal SNR. Given the quantum efficiency $$QE\left( \lambda \right)$$ Q E λ of the detector, we find $${ \mathcal{D}} = 0.015\,{\text{photon}}^{ - 1} \,{\text{s}}^{1/2} \,{\text{cm}}$$ D = 0.015 photon - 1 s 1 / 2 cm , and a non-negligible sensitivity to blackbody radiation for T > 50 °C. This work should help design highly sensitive luminescence detection methods and develop experiments to explore dynamic phenomena involving ultra-weak luminescence in biology, chemistry, and material sciences.


2022 ◽  
Vol 17 (01) ◽  
pp. C01036
Author(s):  
P. Grybos ◽  
R. Kleczek ◽  
P. Kmon ◽  
A. Krzyzanowska ◽  
P. Otfinowski ◽  
...  

Abstract This paper presents a readout integrated circuit (IC) of pixel architecture called MPIX (Multithreshold PIXels), designed for CdTe pixel detectors used in X-ray imaging applications. The MPIX IC area is 9.6 mm × 20.3 mm and it is designed in a CMOS 130 nm process. The IC core is a matrix of 96 × 192 square-shaped pixels of 100 µm pitch. Each pixel contains a fast analog front-end followed by four independently working discriminators and four 12-bit ripple counters. Such pixel architecture allows photon processing one by one and selecting the X-ray photons according to their energy (X-ray colour imaging). To fit the different range of applications the MPIX IC has 8 possible different gain settings, and it can process the X-ray photons of energy up to 154 keV. The MPIX chip is bump-bonded to the CdTe 1.5 mm thick pixel sensor with a pixel pitch of 100 µm. To deal with the charge sharing effect coming from a thick semiconductor pixel sensor, multithreshold pattern recognition algorithm is implemented in the readout IC. The implemented algorithm operates both in the analog domain (to recover the total charge spread between neighboring pixels, when a single X-ray photon hits the border of the pixel) and in the digital domain (to allocate a hit position to a single pixel).


1997 ◽  
Vol 3 (S2) ◽  
pp. 1125-1126
Author(s):  
S.J. Pan ◽  
A. Shih ◽  
W.S. Liou ◽  
M.S. Park ◽  
G. Wang ◽  
...  

An experimental X-ray cone-beam microtomographic imaging system utilizing a generalized Feldkamp reconstruction algorithm has been developed in our laboratory. This microtomographic imaging system consists of a conventional dental X-ray source (Aztech 65, Boulder, CO), a sample position and rotation stage, an X-ray scintillation phosphor screen, and a high resolution slow scan cooled CCD camera (Kodak KAF 1400). A generalized Feldkamp cone-beam algorithm was used to perform tomographic reconstruction from cone-beam projection data. This algorithm was developed for various hardware configuration to perform reconstruction of spherical, rod-shaped and plate-like specimen.A test sample consists of 8 glass beads (approx. 800μm in diameter) dispersed in an epoxy-filled #0 gelatin capsule. One hundred X-ray projection images were captured equal angularly (at 3.6 degree spacing) by the cooled CCD camera at a of 1317×967 (17×17mm2) pixels with 12-bit dynamic range. Figure 1 shows a 3D isosurface rendering of the test sample. The eight glass beads and trapped air bubbles (arrows) in the epoxy resin (e) are clearly visible.


2005 ◽  
Vol 32 (6Part21) ◽  
pp. 2157-2158
Author(s):  
PM Shikhaliev ◽  
T Xu ◽  
S Molloi

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 944 ◽  
Author(s):  
Heesin Lee ◽  
Joonwhoan Lee

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.


1993 ◽  
Vol 306 ◽  
Author(s):  
F. Cerrina ◽  
G.M. Wells

AbstractIn proximity X-ray lithography there is no imaging system in the traditional sense of the word. There are no mirrors, lenses or other means of manipulating the radiation to form an image from that of a pattern (mask). Rather, in proximity X-ray lithography, mask and imaging systems are one and the same. The radiation that illuminates the mask carries the pattern information in the region of the wavefronts that have been attenuated. The detector (photoresist) is placed so close to the mask itself that the image is formed in the region where diffraction has not yet been able to deteriorate the pattern itself. The quality of the image formation then is controlled directly by the interaction between the mask and the radiation field. In turn, this means that both the illumination field and the mask are critical. The properties of the materials used in making the mask thus play a central role in determining the quality of the image. For instance, edge roughness and slope can strongly influence the image by providing the equivalent of a blur in the diffraction process. This blur is beneficial in reducing the high frequency components in the aerial image but it needs to be controlled and be repeatable. The plating (or other physical deposition) process may create variation in density (and thickness) in the deposited film, that will show up as linewidth variation in the image because of local changes in the contrast; the same applies to variations in the carrier membrane. In the case of subtractive process, variations in edge profile across the mask must be minimized.The variations in material composition, thickness and density may all affect the finale image quality; in the case of the resist, local variations in acid concentration may have strong effect in linewidth control (this effect is of course common to all lithographies).Another place where materials will affect the final image quality is in the condensing system. Mirrors will exhibit some degree of surface roughness, leading to a scattered radiation away from the central (coherent) beam. For scanning systems, this is not harmful since no power is lost in the scattering process and a blur is actually created that reduces the degree of spatial coherence. Filters may also exhibit the same roughness; typically it will not affect the image formation. The presence of surface (changes of reflectivity) or bulk (impurities) defects may however strongly alter the uniformity of the transmitted beam. This is particularly true of rolled Be filters and windows, which may include contaminants of high-Z materials. Hence, the grain structure of the window plays a very important role in determining image uniformity.Finally, a seemingly minor but important area is that of the gas used in the exposure area, typically helium. The gas fulfills several needs: heat exchange medium, to thermally clamp the mask to the wafer; low-loss X-ray transmission medium; protection from reactive oxygen radicals and ozone formation. Small amounts of impurities (air) may have a very strong effect on the transmission, and non-uniform distributions are particularly deleterious.All these factors need to be controlled so that the final image is within the required tolerances. Unfortunately, some of these are difficult to characterize in the visible (e.g., reflectivity variations) and testing at X-ray wavelengths is necessary. Although these obstacles are by no means unsurmountable, foresight is necessary in order to deliver a functional X-ray lithography process.This work was supported by various agencies, including ARPA/ONR/NRL and the National Science Foundation.


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