Bibliographic record
Abstract
The problem of nondestructive measurement of composition with depth on the scale of ∼0.1–500 μm, in polymers and related materials, has many applications in traditional and recent areas of thin film processing. This article reviews the optical depth profilometry techniques operating on this scale based on optical absorption, photoluminescence, elastic, and inelastic scattering. These methods include photoacoustic and photothermal imaging (including pulsed laser opto–acoustic profiling), attenuated total reflectance infrared, integrated optical spectroscopy methods (based on excitation of planar waveguide structures), confocal scanning microscopy, and the recent technique of light profile microscopy. The profiling of planar structures is emphasized. A common element of all of these methods is that depth mapping requires the solution of a linear inverse problem, where a map of the sample properties is mathematically reconstructed from a set of experimental measurements. This problem is to some extent ill conditioned in some or all regimes of measurement, with the result that depth maps may show sensitivity to data errors. A method is presented for assessing performance of the above experimental depth profilometry techniques in terms of ill conditioning as indicated by: spatial resolution, sensitivity to data errors, and apparent multiplicity of solutions. This method is applied a priori given a knowledge of the linear response theory and measurement parameters Application is made to individual profiling techniques, the performance of each in applications is reviewed, and an inter-comparison is made based on the conditioning of the inverse problem.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".