Nanowire dopant measurement using secondary ion mass spectrometry
Bibliographic record
Abstract
A method is presented to improve the quantitative determination of dopant concentration in semiconductor nanowire (NW) arrays using secondary ion mass spectrometry (SIMS). SIMS measurements were used to determine Be dopant concentrations in a Be-doped GaAs thin film and NW arrays of various pitches that were dry-etched from the same film. A comparison of these measurements revealed a factor of 3 to 12 difference, depending on the NW array pitch, between the secondary Be ion yields of the film and the NW arrays, despite being identically doped. This was due to matrix effects and ion beam mixing of Be from the NWs into the surrounding benzocyclobutene that was used to fill the space between the NWs. This indicates the need for etched NWs to be used as doping standards instead of 2D films when evaluating NWs of unknown doping by SIMS. Using the etched NWs as doping standards, NW arrays of various pitches grown by the vapour-liquid-solid mechanism were characterized by SIMS to yield valuable insights into doping mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".