Miniaturized 6-Bit Phase-Change Capacitor Bank with Improved Self-Resonance Frequency and $Q$
Bibliographic record
Abstract
This paper reports a 6-bit capacitor bank developed using metal-insulator-metal (MIM) capacitors with enhanced self-resonance frequency (SRF) and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -factor. An easy to implement design optimization technique is discussed to improve the SRF and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> of high frequency MIM capacitors. Experimental data is shown for two MIM capacitors fabricated on high resistivity silicon substrate with silicon nitride as a dielectric layer. The optimized capacitors exhibit up to 45% improved SRF and up to 22% enhanced Q-factor in comparison with standards MIM designs. The capacitor bank utilizes six latching phase change material (PCM) germanium telluride (GeTe)-based RF series switches, monolithically integrated with six MIM capacitors having improved SRF. The capacitor bank measures only <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.23\ \text{mm}\times 0.27\ \text{mm}$</tex> in size, making it a highly miniaturized and versatile switched capacitor bank for integrating with numerous RF circuits. Experimental data is compared with that of standard MIM capacitor-based capacitor bank. The optimized MIM based capacitor bank provides additional 3 GHz operating bandwidth compared to the standard MIM based capacitor bank.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".