High-Resolution Cr/4H-SiC Schottky Barrier Radiation Detector
Bibliographic record
Abstract
In this article, we present the first comprehensive analysis of radiation detection using chromium (Cr) as the Schottky barrier contact on n-type 4H-silicon carbide (4H-SiC) epitaxial layers tailored for high-performance applications in extreme environments. The Cr/4H-SiC Schottky barrier diode (SBD) is evaluated across several critical metrics, including junction properties, radiation response, and defect characteristics, and is compared with SBDs utilizing other refractory metals such as molybdenum (Mo), palladium (Pd), and nickel (Ni) on analogous 4H-SiC epilayers. Despite the lower work function of Cr, 4.5 eV, compared to the other metals, the Cr/4H-SiC SBDs demonstrated exceptional rectification behavior, achieving a barrier height of 1.13 eV and a low leakage current of 6.7 nA at −100 V reverse bias. These characteristics are ideal for high-resolution radiation detection applications. The Cr/4H-SiC SBD exhibited an impressive energy resolution of 0.5% at an optimized bias of −40 V when exposed to 5486-keV alpha particles. Notably, in self-biased mode (0 V applied bias), the device delivered an energy resolution of 2.3% and a charge collection efficiency (CCE) of 73%, surpassing the performance of benchmark Ni/4H-SiC SBDs. Capacitance-mode deep-level transient spectroscopy (DLTS) analysis revealed the presence of key deep-level defects, including Z${}_{1/2}$and EH5 trap centers, and titanium substitutional defects. Among these, the Z${}_{1/2}$trap center, widely regarded as a lifetime-killer, was found to play a significant role in influencing the detector’s performance. The findings in this article highlight the untapped potential of Cr/4H-SiC SBDs for high-efficiency, self-biased radiation detection in harsh environments, such as nuclear reactors and space exploration missions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".