High detectivity terahertz radiation sensing using frequency-noise-optimized nanomechanical resonators
Bibliographic record
Abstract
We achieve high detectivity terahertz radiation sensing using a silicon nitride nanomechanical resonator functionalized with a metasurface absorber. High performances are achieved by striking a balance between the frequency stability of the resonator and its responsivity to absorbed radiation. Using this approach, we demonstrate a detectivity D*≈3.4×109cm⋅Hz/W and a noise equivalent power NEP≈36pW/Hz that outperform the best room-temperature on-chip THz detectors, such as pyroelectric detectors, while maintaining a comparable thermal response time of ≈200 ms. Our optical absorber consists of a 1-mm diameter metasurface, which currently enables a 0.5–3 THz detection range but can easily be scaled to other frequencies in the THz and infrared ranges. In addition to demonstrating high-performance terahertz radiation sensing, our work unveils an important fundamental trade-off between frequency stability and responsivity in thermal-based nanomechanical radiation sensors.
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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 | 0.001 | 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".