NEP characterization and analysis method for THz imaging devices
Bibliographic record
Abstract
Over the last decade, significant progress has been made in the development of Terahertz (THz) imagers to satisfy the growing interest for see-through devices for different market applications. The noise-equivalent power (NEP) is a widely accepted figure of merit used to compare the sensitivity performance of detectors. However, with no widely recognized standard for NEP, it is often difficult to have a fair comparison between different sensors. Having a clear understanding of the characterization method used to calculate this important metric will lead to better estimation of the performances that could be expected from an imaging device. There is some confusion regarding whether NEP should be expressed in terms of power (W) or power by spectral density (W/Hz1/2). The difference between the two expressions is the normalization of the first by the square root of the detector’s equivalent noise bandwidth (ENBW). By properly defining the ENBW for a specific sensor, the translation between the two is then consistent. This paper presents the NEP characterization of INO’s Microxcam-384i camera over a wide frequency range. A description of the measurement setup is provided, as well as the details of the analysis method, including the estimation of the ENBW. Finally, values for the NEP using both expressions are provided for wavelengths between 70μm (4.5 THz) and 1.5mm (198 GHz), demonstrating the broadband sensitivity of the camera.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".