$D$-Band Total Power Radiometer Performance Optimization in an SiGe HBT Technology
Bibliographic record
Abstract
A D-band SiGe HBT total power radiometer is reported with a peak responsivity of 28 MV/W, a noise equivalent power (NEP) of 14-18 fW/Hz1/2, and a temperature resolution better than 0.35 K for an integration time of 3.125 ms. The 1/f noise corner of the radiometer is lower than 200 Hz. Fabricated in a developmental technology with 270-GHz fTand 330-GHz/max, it includes a five-stage low-noise amplifier (LNA) with 4-7-GHz bandwidth and over 35 dB of gain centered at 165 GHz, along with a square-law detector with an NEP below 6 pW/Hz1/2up to 170 GHz. An average system noise temperature of 1645 K is obtained using the Y-factor method and a noise bandwidth of 10 GHz calculated from the measured S21(f) characteristics of the radiometer. The reduced 1/f noise corner frequency in the presence of the amplifier, compared to that of the detector, appears to indicate that, unlike in III-V radiometers, LNA gain fluctuations are not a problem in SiGe HBT radiometers. The circuit consumes 95 mW and occupies 765 × 490 μm2. Wafer mapping of the radiometer sensitivity and of the amplifier gain was performed across different process splits. The mapping results demonstrate that the radiometer can be employed as a relatively simple and area-efficient transistor noise-measure monitor, useful in SiGe HBT vertical profile optimization.
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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 | 0.000 | 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".