A Low-Power 136-GHz SiGe Total Power Radiometer With NETD of 0.25 K
Bibliographic record
Abstract
This paper presents a low-noise SiGe radiometer at 136 GHz developed in an IBM 90-nm SiGe BiCMOS technology. The radiometer consists of a three-stage cascode low-noise amplifier with a gain of 36 dB, and a differential output square-law detector, all on a single chip. The detector results in responsivity of 11 kV/W and a noise equivalent power (NEP) of 0.6 pW/Hz1/2at D-band frequencies. The radiometer chip consumes 45 mW and results in a minimum NEP of 1.4 fW/Hz1/2with a peak responsivity of 52 MV/W at 136 GHz. The single-chip radiometer is suitable for high-resolution imaging systems having a noise bandwidth > 10 GHz and a low 1/f corner frequency . For an integration time of 3.125 mS (τ = 3.125 mS), the temperature resolution [noise equivalent temperature difference (NETD)] is determined to be 0.25 K using several different independent methods, and is the lowest NETD demonstrated in silicon technologies at D-band frequencies. This state-of-the-art performance is comparable to the best III-V imaging systems and proves that the advanced SiGe technology is a reliable option for imaging and radiometry applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".