The impact of electric field strength on the accuracy of boron dopant quantification in silicon using atom probe tomography
Bibliographic record
Abstract
) was used as an indirect measure of the average apex electric field during analysis. For a range of electric fields, the accuracy of the total implanted dose and the depth profile shape determined by APT was evaluated against the National Institute of Standards and Technology Standard Reference Material 2137. The radial (non-)uniformity of the detected B was also examined. At a higher surface electric field (i.e., a greater CSR(Si)), the determined B dose converges on the certified dose. Additionally, the depth profile shape tends towards that derived by secondary ion mass spectrometry. This improvement coincides with a more uniform radial B distribution, evidenced by desorption maps. In contrast, for lower surface electric fields (i.e., a lower CSR(Si)), the B dose is significantly underestimated, and the depth profile is artificially stretched. The desorption maps also indicate a highly inhomogeneous B emission localized around the center of the detector, which is believed to be an artifact of B surface migration on the tip of the sample. For the purposes of routine investigations of semiconductor devices using APT, these results illustrate the potential origin of quantification artifacts and their severity at different operating conditions, thus providing pathways towards best practices for accurate and repeatable measurements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".