Thin film resistors and capacitors for multichip modules
Bibliographic record
Abstract
A significant advantage of using thin film, rather than laminate technology, for MCMs is the ability to incorporate passive components, resistors, capacitors and spiral inductors at low cost. Tantalum-silicon alloy resistors and silicon nitride capacitors have been widely used but modifications to traditional processing have greatly improved the robustness of the process. The use of NF/sub 3/ gas for tantalum silicide etching provides excellent sidewall geometry and uniformity over the whole of the wafer so that narrow lines can yield high-tolerance resistors. It also fulfils the requirements of the Montreal Convention. Silicon nitride capacitors are formed using plasma enhanced chemical vapour deposition (PECVD) instead of low pressure chemical vapour deposition so as to reduce the temperature of deposition from 785/spl deg/C to 400/spl deg/C. This minimises oxidation of the tantalum silicide and the associated resistor drift. The PECVD nitride provides pinhole free capacitors with a yield of >99% up to 3 mm square. Breakdown strength is in excess of 1.7/spl times/10/sup -6/ V/cm. PECVD also provides excellent uniformity, <2% over a 150 mm wafer. The values of resistors fall by 5% during polyimide cure at 400/spl deg/C but there is no widening of the distribution so the tolerance is not affected. The thermal coefficient of resistance is less than 100 ppm/K over the temperature range 25-175/spl deg/C.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.020 |
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".