A Packaged THz Shunt RF MEMS Switch With Low Insertion Loss
Bibliographic record
Abstract
This paper demonstrates a packaged THz shunt capacitor micro-electromechanical systems (MEMS) switch with low insertion loss. In-line shunt switch is used to achieve a low loss in THz band, which is realized by reducing the equivalent parallel inductance of switch. The equivalent circuit of the switch is analyzed systematically, the equivalent resistance is obtained based on the skin effect of high frequency current on the conductor and the current density distribution characteristics of the conductor cross-section. The equivalent capacitance is obtained by using “double microstrip” characteristic impedance calculation method, and the correction factor ($\Delta$) is introduced to calculate the equivalent inductance accurately. By optimizing equivalent circuit parameters and switch sizes, the structure of MEMS switch with low loss and high isolation characteristics are achieved. The switch is packaged by rectangular waveguide and achieved a low insertion loss. The comparison between the switch simulation results and the equivalent circuit simulation results verify that the parameter extraction method and circuit analysis are correct. The packaged MEMS switch is measured, and the results are in an acceptable agreement with simulation, the switch is actuated under voltage of ~30V. The measured result has achieved a low insertion loss with less than < 2dB from 220 to 280GHz, and isolation with ~16 dB from 240GHz to 320GHz in the “down” state.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".