Calibration of microscope-coupled Fourier transform infrared spectrometers for CW and modulated light emission measurements
Bibliographic record
Abstract
Measurement of low power infrared light emission spectra from microstructures can be challenging but is of key importance in several research fields. Fourier transform infrared (FTIR) spectrometers can be used for characterizing such weak light emitters, but this requires additional custom user calibration compared to traditional FTIR measurements of, e.g., transmission or reflection. These calibration techniques are well documented for standalone FTIR instruments but not for microscope-coupled FTIRs, even though such an architecture greatly simplifies the collection of light from micro- and nanoscale structures. We propose and demonstrate a calibration method for microscope-FTIRs based on the well-known emissivity of doped silicon at high temperatures. With this method, we measure the responsivity and noise floor of a recently installed microscope-FTIR instrument (Bruker© Invenio® R coupled with a Hyperion II microscope), which is found to be within theoretically predicted values. The method is demonstrated for two different detectors (mercury cadmium telluride and indium antimonide), in both continuous wave and modulated (step-scan) emission measurement modes.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".