P3I: a simulation code for Plasma Immersion Ion Implantation (PIII) dose prediction
Bibliographic record
Abstract
Plasma Immersion Ion Implantation (PIII) is a versatile material processing technique [1] , [2] with many applications in semiconductor doping and micro- and nano- fabrication [3] , as well as the surface modification of metals for improved resistance against wear and corrosion. In PIII a solid target is immersed in plasma, and negative polarity high voltage (typically 1-20 kV) pulses are applied to the target. During a negative-polarity PIII pulse electrons are repelled from the region near the target, resulting in a positive ion sheath surrounding the target; ions traversing the sheath are implanted into the solid target surface. PIII can provide uniform ion implantation with high ion fluences across broad area targets. The targets need not be planar as the plasma is conformal to the immersed target. For precision PIII processing it is important to accurately predict the implanted ion concentrations. To this end, the P2I code was developed by Bradley, Steenkamp, and Risch [4] , [5] to accurately predict PIII sheath dynamics, ion implantation currents, and total delivered ion fluences. The P2I code is an efficient implementation of the numerical solution of Lieberman's dynamic sheath model [2] with assumptions of quasi-static ion motion and sheath position, collision less ion flow, inertia less electrons and an infinite plasma ion reservoir.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".