A New Approach to Enhancing Radiation Hardness in Advanced Nuclear Radiation Detectors Subjected to Fast Neutrons
Bibliographic record
Abstract
Low-Gain Avalanche Diodes (LGADs) are critical sensors for the ATLAS and CMS timing detectors at the High Luminosity Large Hadron Collider (HL-LHC), offering enhanced timing resolution with gain factors of 20 to 50. However, their radiation tolerance is hindered by the Acceptor Removal Phenomenon (ARP), which deactivates boron in the gain layer, reducing gain below the threshold for accurate timing. This study investigates the radiation hardness of thin, carbon-doped LGAD sensors developed by Brookhaven National Laboratory (BNL) to address ARP-induced limitations. Active dopant profiles in the gain layer, junction, and bulk were measured using a Spreading Resistance Probe (SRP) profilometer, and the effects of annealing and neutron irradiation at fluences of 3 × 1014, 1 × 1015, and 3 × 1015 neq/cm2 (1 MeV equivalent) were analyzed. Low carbon dose rates showed minimal improvement due to enhanced deactivation, while higher doses improved radiation hardness, demonstrating a non-linear dose–response relationship. These findings highlight the potential of optimizing gain layers with high carbon doses and low-diffusion boron to extend LGAD lifetimes in high-radiation environments. Future research will refine carbon implantation strategies and explore alternative approaches to further enhance the radiation hardness of LGADs.
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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 | 0.001 |
| 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 | 0.001 |
| 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".