A GaN-Based Wireless Monitoring System for High-Temperature Applications
Bibliographic record
Abstract
A fully-integrated data transmission system based on gallium nitride (GaN) high-electron-mobility transistor (HEMT) devices is proposed. This system targets high-temperature (HT) applications, especially those involving pressure and temperature sensors for aerospace in which the environmental temperature exceeds 350 °C. The presented system includes a front-end amplifying the sensed signal (gain of 50 V/V), followed by a novel analog-to-digital converter driving a modulator exploiting the load-shift keying technique. An oscillation frequency of 1.5 MHz is used to ensure a robust wireless transmission through metallic-based barriers. To retrieve the data, a new demodulator architecture based on digital circuits is proposed. A 1 V amplitude difference can be detected between a high-amplitude (data-on) and a low-amplitude (data-off) of the received modulated signal. Two high-voltage supply levels (+14 V and −14 V) are required to operate the circuits. The layout of the proposed system was completed in a chip occupying 10.8 mm2. The HT characterization and modeling of integrated GaN devices and passive components are performed to ensure the reliability of simulation results. The performance of the various proposed building blocks, as well as the whole system, have been validated by simulation over the projected wide operating temperature range (25–350 °C).
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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) | 0.002 | 0.001 |
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".