Defect level identification of ATLAS ITk Strip Sensors using DLTS
Bibliographic record
Abstract
As a part of ongoing studies in parallel to ITk Strip Sensor Production quality control (QC) and quality assurance (QA), diodes fabricated as test structures were measured using \\emph{Deep-Level Transient Spectroscopy} (DLTS). This was done to achieve precise sensor simulations motivated by findings of anomalous leakage current behaviour, as well as to compile a more complete model of radiation damage in ITk Strip Sensors. Utilising DLTS spectra with varying test parameters, trap energy levels and cross-sections associated with defects in the devices were obtained. Furthermore, employing related measurements techniques, such as Thermal Admittance Spectroscopy (TAS), results were supplemented and expanded, or additional points of interest, such as the deep level profile and the capture kinematics of the trap levels, were investigated with double-pulse DLTS (DDLTS). A common trap consistent with a $\\mathrm{C_i O_i}$ point defect in unirradiated diodes was identified, but also an additional trap suspected to cause high leakage current. In proton-irradiated diodes, defects related to vacancy clusters or a boron-hydrogen complex were observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".