An Effective Interlayer Dielectric and Passivation Scheme Using Reactively Sputtered AL 2 O 3 for (Ba,Sr)TiO 3 Capacitors
Bibliographic record
Abstract
Interlayer dielectric and passivation layers for BST capacitors are often very hydrogen rich as a result of the by-products generated during the fabrication process. This hydrogen is well known to significantly degrade the leakage characteristics of the underlying BST capacitors. [1] While it is possible to focus on modifying interlayer dielectric (ILD) or passivation processes to minimize hydrogen exposure, it is preferable to maintain standard process modules available in silicon fabrication lines for case of manufacturing. However, post-deposition annealing is frequently required to reduce the effects of hydrogen, which may migrate into the capacitor during these deposition processes. It is known that Al 2 O 3 films provide an effective barrier to hydrogen migration, even at high temperatures. This paper discusses the integration of a reactively sputtered Al 2 O 3 barrier layer into the interlayer dielectric and passivation process flows of BST thin film capacitors to reduce device degradation during backend processing. Reactively sputtered Al 2 O 3 films were integrated into the production ILD process flow for BST thin film capacitors. Results indicate significant reduction in the post-deposition annealing time is possible while maintaining stable I-V characteristics on the finished devices. The barrier layer can also be etched by standard RIE tools used to etch other common oxides in silicon processing. Aggressive backend passivation schemes were also evaluated to determine the process window available for robust backend integration.
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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 | 0.001 | 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".