Epitaxial 4H-SiC Radiation Detectors for Harsh Environment Applications

Epitaxial 4H-silicon carbide (4H-SiC) is an essential semiconductor material for the development of harsh environment radiation detectors due to its excellent electrical and thermal properties and resistance to radiation damage, opening the door for a wide variety of applications in NASA space missions, nuclear safeguards, and nuclear energy. However, the low atomic numbers of its constituent atoms Si (Z = 14) and C (Z = 6) make 4H-SiC nearly transparent to most neutrally charged radiation which can only be compensated using thicker active volumes. In this dissertation, Ni/n-4H-SiC Schottky barrier diode (SBD) radiation detectors are fabricated for the first time on 250 µm thick 4H-SiC epitaxial layers—the thickest epilayers used for radiation detection to date. The detectors were found to have low leakage current densities-800 V and benchmark 5486 keV alpha particle energy resolutions of1/2. Further studies were conducted to characterize the effects of harsh environment conditions on the properties of the detectors. First, temperature variation of the leakage current at elevated temperature was studied by temperature-dependent current voltage (IV-T) measurements on 150 µm epitaxial layers which revealed that traps such as Z1/2 and EH6/7 and low barrier patches in spatial geometry of the metal-semiconductor (M-S) interface can produce excess leakage current compared to thermionic emission-diffusion (TED) theory. Next, the effect of neutron irradiation up to fluences of 1013 cm-2 was studied using 250 µm epilayers. Detector energy resolution was shown to degrade with increasing fluences which was correlated with the formation of three new deep levels at 0.8, 1.2, and 1.8 eV below the conduction band. These levels were found to correspond to—based on density functional theory calculations with hybrid pseudopotentials—silicon displacement-related defects formed from the collision of fast neutrons (\u3e 1 MeV) with the silicon nucleus

Advances in High-Resolution Radiation Detection Using 4H-SiC Epitaxial Layer Devices

Advances towards achieving the goal of miniature 4H-SiC based radiation detectors for harsh environment application have been studied extensively and reviewed in this article. The miniaturized devices were developed at the University of South Carolina (UofSC) on 8 × 8 mm 4H-SiC epitaxial layer wafers with an active area of ≈11 mm2. The thicknesses of the actual epitaxial layers were either 20 or 50 µm. The article reviews the investigation of defect levels in 4H-SiC epilayers and radiation detection properties of Schottky barrier devices (SBDs) fabricated in our laboratories at UofSC. Our studies led to the development of miniature SBDs with superior quality radiation detectors with highest reported energy resolution for alpha particles. The primary findings of this article shed light on defect identification in 4H-SiC epilayers and their correlation with the radiation detection properties

A CdZnTeSe Gamma Spectrometer Trained by Deep Convolutional Neural Network for Radioisotope Identification

We report the implementation of a deep convolutional neural network to train a high-resolution room-temperature CdZnTeSe based gamma ray spectrometer for accurate and precise determination of gamma ray energies for radioisotope identification. The prototype learned spectrometer consists of a NI PCI 5122 fast digitizer connected to a pre-amplifier to recognize spectral features in a sequence of data. We used simulated preamplifier pulses that resemble actual data for various gamma photon energies to train a CNN on the equivalent of 90 seconds worth of data and validated it on 10 seconds worth of simulated data

Quaternary Semiconductor Cd1−xZnxTe1−ySey for High-Resolution, Room-Temperature Gamma-Ray Detection

The application of Cd0.9Zn0.1Te (CZT) single crystals, the primary choice for high-resolution, room-temperature compact gamma-ray detectors in the field of medical imaging and homeland security for the past three decades, is limited by the high cost of production and maintenance due to low detector grade crystal growth yield. The recent advent of its quaternary successor, Cd0.9Zn0.1Te1−ySey (CZTS), has exhibited remarkable crystal growth yield above 90% compared to that of ~33% for CZT. The inclusion of Se in appropriate stoichiometry in the CZT matrix is responsible for reducing the concentration of sub-grain boundary (SGB) networks which greatly enhances the compositional homogeneity and growth yield. SGB networks also host defect centers responsible for charge trapping, hence their reduced concentration ensures minimized charge trapping. Indeed, CZTS single crystals have shown remarkable improvement in electron charge transport properties and energy resolution over CZT detectors. However, our studies have found that the overall charge transport in CZTS is still limited by the hole trapping. In this article, we systematically review the advances in the CZTS growth techniques, its performance as room-temperature radiation detector, and the role of defects and their passivation studies needed to improve the performance of CZTS detectors further

Synthesis of CdZnTeSe Single Crystals for Room Temperature Radiation Detector Fabrication: Mitigation of Hole Trapping Effects Using a Convolutional Neural Network

We report the growth of Cd0.9Zn0.1Te0.97Se0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd0.9Zn0.1Te0.97Se0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored