Electron backscattering for signal enhancement in a thin‐film CdTe radiation detector

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PurposeThe characterization of electron backscattering is essential in medical physics for accurately assessing dose deposited around inhomogeneities where backscattering alters the spatial energy distribution pattern and for determining Monte‐Carlo code's ability to effectively describe electron scattering and does calculation in a target volume. Recent machine learning advances have provided physicists with powerful tools for effectively extracting information and trends from extensive experiment observations if sufficiently sizeable datasets are available for data mining. We report on the development of a publicly accessible database on electron backscattering coefficients for solid targets.Acquisition and validation methodsThe first database on electron‐solid interactions was assembled in 1995. Data for bulk materials, limited to normal incidence and energies up to 100 keV, were primarily focusing on electron microscopy. To accommodate broad high‐energy applications and include the most recent publications we have created a comprehensive database of electron backscattering coefficients, listed as a function of target atomic number and thickness, electron energy, and incidence angle. These additions resulted in a database of 3566 data points, compared to the previous database of 1430. The data collection includes only published experimental observations (no calculations or results fitting) with no attempt to judge their accuracy or quality. A limited number of data points were compared to recently published Monte‐Carlo results.Data format and usage notesThe presented database provides values of electron backscattering coefficients for 50 elements and 19 compounds at electron energies ranging from 0.1 keV to 15 MeV, presented in ASCII files. Each file contains the electron energy and backscattering coefficient with target thickness or electron incidence angle included where available, and the reference number shown in the last column. Additionally, the presented data were shown in the graphs for better visualization. The online database can be accessed from the website https://doi.org/10.5281/zenodo.7810951.Potential applicationsThe database provides the most up‐to‐date source of experimentally obtained electron backscattering coefficients that can be used in theoretical and MC calculations and modeling validations. The data availability is still very limited for many solids and almost non‐existent for compounds. Novel machine learning methods should be well adapted to predict these unknown values for various targets, thicknesses, energies, and incident angles utilizing the presented cleaned dataset.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Acquisition and Validation Methodsmentioning

confidence: 99%

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A comprehensive open‐access database of electron backscattering coefficients for energies ranging from 0.1 keV to 15 MeV

Akbari

2023

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Electron backscattering for signal enhancement in a thin‐film CdTe radiation detector

Akbari

Shvydka

2022

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Background: Thin-film cadmium telluride (CdTe) offers high average electron density, direct detection configuration, and excellent radiation hardness, making it an attractive material for radiation detectors. Although a very thin detector provides capabilities to conduct high-resolution measurements in high-energy radiation fields, it is limited by a low signal, often boosted with a front metal converter enhancing X-ray absorption. An extension of this approach can be explored through the investigation of electron backscattering phenomenon, known to be highly dependent on the material atomic number Z. Adding an electron reflector in tandem with the back electrode is proposed to be utilized for the detector signal enhancement. Purpose: We investigated the possibility of augmenting the fluence of electrons traversing CdTe thin film and thus increasing the detected signal pursuing two pathways: (1) adding a high-Z metal layer to the back of the detector surface, and (2) adding a top low-Z material to the detector layer to return its backscattered electrons. Copper (Cu) and lead (Pb) layers of varying thickness were investigated as potential metal back-reflectors, whereas polymethyl methacrylate (PMMA) water phantom material was tested as the top cover in multilayer detector structures. Methods: The Monte Carlo (MC) radiation transport package MCNP5 was first used to model a basic multilayer structure of a CdTe-sensitive volume surrounded by PMMA, under a 6-MV photon beam. Addition of Cu or Pb back-reflectors allowed for the analysis of the signal enhancement and associated changes in Compton electrons fluence spectra. Related backscattering coefficients were then calculated using EGSnrc MC user-code for monoenergetic electron sources. Analytical functions were established to represent the best fitting curves to the simulation data. Finally, electron backscattering data was related to signal enhancement in the CdTe sensitive layer based on a semiquantitative approach. Results: We studied multilayer detector structures, decoupling the effects of PMMA and the back-reflector metals, Cu or Pb, on electron backscattering for electron energy range of up to 500 keV or 1 MeV depending on the choice of metal. Adding a 100-200-µm-thick metal film below the detector sensitive volume increased the fraction of reflected electrons, especially in the low, 100-200 keV, energy range. The thickness dependence of backscattering coefficients from thin films exhibits saturations at values significantly exceeding the electron ranges. That effect was related to the large-angle electron scattering. A

Prediction of electron‐solid interaction parameters using machine learning

Akbari

2024

BackgroundElectron backscattering coefficient and electron‐stopping power are essential concepts in many disciplines, from radiation to materials science, semiconductor manufacturing, and space exploration. They enable precise calculations, measurements, and simulations of electron interactions with matter, which contribute to advancing science, technology, and safety in a variety of applications. The availability of these data is fundamental to scientific research to validate hypotheses, conduct experiments, and explore new theories. A relatively novel machine learning approach has demonstrated notable success in enhancing data quality and completeness, significantly contributing to the facilitation of data discovery.PurposeUsing fundamental material property data, the stacking ensemble machine learning (EML) technique was established in this study to generate electron‐solid interaction parameters for any target material over a wide range of energies. The final stacking EML was built using the base and meta learners bagging regressor (BR), K‐nearest neighbors (k‐NN), random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGB).MethodsIn this study, two publicly available databases with a total of 4030 data points were used. Training datasets have 785 and 525 data points for electron backscattering coefficient and stopping power, respectively, whereas testing datasets contain 262 and 175 data points. Five features were used as input variables to train different individual algorithms and their combinations. On both the training and test datasets, the model was evaluated using different error metrics, including R‐squared (R2), mean‐absolute‐error (MAE), root‐mean‐squared‐error (RMSE), and mean‐absolute‐percentage‐error (MAPE).ResultsOur model evaluation tests revealed that combining RF and XGB with a k‐NN meta‐learner outperformed other algorithms. The analysis of error metrics demonstrated a very close fit to all samples in each training dataset. Furthermore, predictions made by the model on unseen test data indicated accurate estimations of new backscattering and stopping power data.ConclusionsThe developed model achieved high prediction accuracy for various target materials across the broad electron energy spectrum. The outcomes demonstrate the effectiveness of machine learning methodology and the chosen models' suitability for addressing substantial physics challenges.