ON THE CHARACTERIZATION OF ELECTRONICALLY ACTIVE DEFECTS IN HIGH-к GATE DIELECTRICS
Bibliographic record
Abstract
A number of techniques are discussed with regard the measurement of electronically active defects in high-к gate dielectrics. Following a short review of 1st-order trapping kinetics, a discussion of its limitations, especially with respect to high-к gate dielectrics is included. It is suggested that, due to the nature of the proposed trap modulated transport in high-к gate dielectrics, the 1st-order trapping kinetics used historically for SiO2, may not be applicable without significant revision. However, the measurement techniques using the standard “stress and sense” methodology, where charge is injected into the film and the effects of the charge trapping are measured with either capacitancevoltage (ΔVFB) and/or current-voltage (ΔVt) to measure the effects of the charge trapping may still be applicable if reasonable assumptions may be made. A discussion of the positioning of trapped charge (i.e. bulk vs interface) is included. Data from HfO2 using electron injection via internal photo-emission and charge centroid extraction using the “photo I-V” technique suggest that centroid of the trapped charge is within the bulk of the high-к film independent of bias polarity and photon energy. Techniques involving transient current analysis using charge pumping in both “conventional base sweep” and “amplitude sweep” and pulsed IDS-VG are also presented. While these techniques are capable of measuring interfacial and near-interfacial trapped charge, their usefulness for obtaining a full understanding of spatial or energetic trap distributions is limited.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".