Analysis, Modeling, and Mitigation of Parasitic Resonances in Integrated Metallic Seal Rings
Bibliographic record
Abstract
The cause of a measured -1.4-dB dip at 80 GHz in a silicon capacitor was investigated and determined to be the primary resonance of the integrated metal seal ring (SR) enclosing the component. Specifically, the proximity of a capacitor electrode to the SR was found to activate two parasitic, parallel, and opencircuited coplanar strip transmission lines (TLs), each having their λ/2 resonance at this frequency. This model was confirmed with 3-D finite-element method electromagnetic simulations and measurements of modified SRs. Building on the proposed TL interpretation of the problem, an ultra-broadband equivalent circuit model was developed for such capacitors, successfully replicating the measured SR resonance. Then, the fundamental resonance frequency and related quality factor of various-sized integrated metal SRs were accurately predicted. Finally, potential solutions to damp or cancel such parasitic resonances were investigated. Although cutting the SR at selected locations to disable the parasitic lines was experimentally proven to be successful, damping the parasitic signal with highly doped silicon is found to be the best option in terms of effectiveness and practical implementation. Novel solutions, analogous to the use of radio frequency terminations, were also tested. This paper, to the best of our knowledge, is the first extensive characterization of parasitic resonance afflicting electronics enclosed by an integrated metal SR. The analysis, equivalent circuit model, and solutions reported here can be directly applied to arrays of components or circuits, and are scalable to any device size and operating frequency.
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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) | 0.000 | 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".