Tomographic measurement of buried interface roughness
Bibliographic record
Abstract
The authors demonstrate that electron tomography allows accurate measurement of roughness of buried interfaces in multilayer samples. The method does not require the interface to be exposed at the surface of the sample, or does it require a laterally extended sample. Therefore, it enables quantitative site specific analysis of individual elements within semiconductor devices. The standard deviation of the interface distance from a plane fitted to an interface is used as a measure of the interface roughness. The roughness is evaluated in three dimensions, eliminating the uncertainties inherent to roughness measurements on cross-sectional images from a single projection. The apparent interface roughness depends on the signal-to-noise ratio (S/N) arising from electron counting statistics in the data. To eliminate the effect of the S/N, multiple images were collected at each tilt. The roughness was extrapolated to an asymptotic value with a high S/N. This value was taken as the true interface roughness. The method was validated on computer generated data by demonstrating a good agreement between known roughness values and asymptotic values obtained using the above method.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".