Evaluation of Silicide Morphology by Near-Infrared-Laser Optical-Beam-Induced-Current Technique
Bibliographic record
Abstract
We found that the near-infrared-laser optical-beam-induced-current (IR-OBIC) technique was very useful for the evaluation of silicide morphology in ultralarge-scale integrated (ULSI) devices. By this technique, it is possible to detect the cohesion points of silicide as two-dimensional images by scanning a near-infrared laser from the back of the chip. The cohesion points appear as bright spots. We confirmed that the number and intensity of bright spots changed according to the extent of cohesion for some different samples upon varying the silicide layer thickness or thermal treatment time after silicide formation. Furthermore, other experiments were performed to clarify the image formation mechanism at cohesion points. It was demonstrated that the electromotive current was generated upon irradiation by the near-infrared-laser, and Schottky junctions were formed at cohesion points. Thus, it was clarified that the images obtained at cohesion points by this technique are a result of the electromotive current generated due to the carriers (electrons or holes) that are excited over the Schottky barrier formed at cohesion points. The IR-OBIC technique can be used to detect the silicide morphology nondestructively without the need to remove the upper layers of the silicide layer. This study reveals a novel application of the IR-OBIC method which is a very useful technique for the evolution of the self-aligned silicide (SALICIDE) process or structure in future ULSIs.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".