TU‐C‐224C‐03: Dose Dependence of MOSFET Calibration Factor Between 30kV and Cobalt‐60 Irradiation
Bibliographic record
Abstract
Purpose: To characterize the behavior of MOSFETs under radiation of various clinically relevant energies used in radiotherapy and radiology by evaluating its sensitivity or threshold voltage shift (CF) with regard to total integrated dose. Method and Materials: Seven p‐type, duals bias MOSFETs from Thomson & Nielsen were investigated. They were exposed to four radiations sources: (1) 60Co unit (〈E〉γ: 1.25 MeV), (2) 192Ir HDR unit (〈E〉γ;: 0.38 MeV), 30 kV beam (〈E〉γ: 14.8 keV) and (4) 150 kV beam (〈E〉γ: 70.1 keV). The MOSFET's sensitivity (CFw) was evaluated at various moments in time and was calculated as the ratio of the measurement Mw (mV) over the estimated dose value Dw (cGy) both in water. Results: The sensitivity of MOSFET is express by their calibration factor (CFw), and allows the user to associate the reading displayed by the device (mV) to a dose value (cGy). The CFw value diminishes with increasing threshold voltage, especially for low energy radiation. It is stable for 60Co irradiations, while it decreases of 6%, 5% and 15% for beam energies of 192Ir, 150 kV and 30 kV respectively. This behavior is explained by an alteration of the effective field applied on the MOSFET (bias), caused by the accumulation of holes at the SiO2 interface. It is strongly dependent on the radiation nature (LET) and particularly affects low x‐ray energies. Conclusions: Those results are of major interest since, following the company recommendations to calibrate the device every 7 000 mV, it could lead to a significantly underestimated dose. A calibration of the device before every use and performing more than one measurement (thus using a mean dose value) should compensate the observed behavior.
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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 | 0.000 |
| Open science | 0.000 | 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".