Development of the large current cluster ion beam technology
Bibliographic record
Abstract
A cluster is an aggregate of a few to several thousands atoms. Because many atoms constituting a cluster ion bombard a local area, high-density energy deposition and multiple-collision processes are realized. Because of the interactions, cluster ion beam processes can produce unusual new surface modification effects, such as surface smoothing, high rate sputtering and very shallow implantation. High ion dose is needed to realize the nano-level smoothing and etching of hard materials. It is necessary to develop the technology of large current cluster ion beam for the purpose of increasing the productivity of processing using cluster ion beam. In order to achieve large current cluster ion beam, the cluster generation, ionization and ion transportation were studied. The efficient cluster generation was realized by increase of source gas pressure. The mass distributions of Ar cluster beams generated from a Laval nozzle were measured with a Time-of-Flight (TOF). The distributions prove that the neutral beams include clusters with the size up to 160000 atoms. The efficient ionization and extraction were realized by structural improvement of the ionizer. The ionization efficiency increases with the emission current. When the emission current is 100mA, the ionization efficiency reaches about 80 %. The structural improvements of ionizer suppressed the loss of ions by coulomb repulsion and realized the extraction of large current cluster ion beams. As a result, when the gas pressure was 15000 Torr and the electron emission current is 300 mA, the beam current reached 500 μA. The cluster ion dose of more than 1×1016ions/cm2is needed to realize the nano-level smoothing. With this beam current, 6 inches wafers can be treated for about 10 minutes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".