Intercomparison of silicon dioxide thickness measurements made by multiple techniques: The route to accuracy
Bibliographic record
Abstract
A pilot project has been launched under the auspices of the Consultative Committee for Amount of Substance to evaluate the quantification for SiO2 on (100) and (111) Si in the thickness range 1.5–8 nm. Wafers and methodologies have been carefully prepared. Forty-five sets of measurements have been made in different laboratories using eight methods: medium energy ion scattering spectrometry (MEIS), nuclear reaction analysis (NRA), Rutherford backscattering spectrometry (RBS), elastic backscattering spectrometry (EBS), x-ray photoelectron spectroscopy (XPS), ellipsometry, grazing incidence x-ray reflectometry (GIXRR), neutron reflectometry (NR), and transmission electron microscopy. The results have been assessed, against the National Physical Laboratory (NPL) XPS data, using d(respondee)=md(NPL)+c. All show excellent linearity. The main sets correlate with the NPL data with average root-mean-square scatters of 0.13 nm with half being <0.1 nm. Each set allows the relative scaling constant, m, and the zero thickness offset, c to be determined. Each method has 0<c<1 nm and it is these offsets, measured here, that have caused many problems in the past. Each technique has a different accuracy for m and consistent results have been achieved. XPS has poor accuracy for m but a high precision and, critically, has zero offset if used correctly. Achieving a consistent scaling constant and zero offset for XPS requires reference conditions that dramatically improve data consistency and validity. A combination of XPS and other methods allows an accurate determination of the XPS scaling constant, m=0.986±0.004. XPS may then have an uncertainty approaching 2%, traceable via other methods. NR, GIXRR, MEIS, NRA, RBS, and EBS have small offsets which, if they can be controlled, will enable these methods also to be used with high accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".