RHBD current-mode bandgap with SET isolation using PVT-independent sensors
Bibliographic record
Abstract
This manuscript introduces a single event transient (SET) detecting circuit which is used in a current- mode bandgap reference circuit to reduce the magnitude of SET-induced voltage pulses at the bandgap output. Switches controlled by the SET detectors are inserted between the bandgap and output. When either a positive or negative voltage transient is detected at bandgap, one of the switches will be turned off to temporarily isolate the bandgap circuit from the output, thus preventing the SET glitches from propagating to the load devices. A capacitor at the output was used to keep the output voltage stable in case of an SET. Once the collected charge is dissipated and the bandgap reference circuit resumes normal operation, the switches will be turned on so that normal reference voltage will be reconnected to the output. This proposed structure was fabricated in a 28-nm FDSOI technology. Simulated results revealed a significant reduction in the SET magnitude. These results were also validated experimentally by using a 105 MeV proton radiation facility, and the SET magnitude at the bandgap reference output can be limited to 10 mV. The implemented SET detector is a versatile structure that can be applicable to DC circuits including LDOs, DC-DC converters and other types of bandgap reference circuits, enhancing their reliability when operating in high radiation environments.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 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".