Bibliographic record
Abstract
Introduction This chapter outlines the principles, advantages and limitations of the methods in use for the characterization of extended defects and should enable the reader to appreciate the experimental results presented. For additional accounts see Brundle et al . 1992, Yacobi et al . 1994, Schroder 1998, and Runyan and Shaffner 1998. Characterization methods can be classified as (i) either surface or bulk techniques, as (ii) either destructive or non-destructive methods, and as (iii) either requiring the application of contacts or not. We have excluded the free surface from consideration on the grounds that, like point defects, its study constitutes a large specialized field already covered by many publications. We therefore also omit surface microscopy and analysis techniques here. Generally, non-destructive techniques are preferred as are contactless ones. However, in practice neither is a very important factor as generally one or a few specimens can be sacrificed for destructive examination and contacts can usually be applied. Characterization techniques are essential for failure analysis and quality control of semiconductor materials and devices. Often failure modes are associated with manufacturing process-induced defects or with defect-dependent device degradation in service. Electrical measurements for the analysis of a wide range of semiconductor transport properties such as, for example, resistivity (conductivity) , Hall effect and capacitance-voltage measurements are made on whole bulk specimens and devices. The net influence on these properties of all the defects present then appears in the results.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".