Characterization, Optimization, and Fabrication of Phase Change Material Germanium Telluride Based Miniaturized DC–67 GHz RF Switches
Bibliographic record
Abstract
This paper presents the characterization, optimization, and fabrication of phase change material (PCM) germanium telluride (GeTe) based RF switches investigating the materials' aspect and design parameters of the switches and their impact on the RF performance. Surface properties of GeTe thin films are investigated through atomic force microscopy (AFM), scanning electron microscopy (SEM), and cross-wafer resistance mapping measurements. Optimized GeTe thin films exhibit over five orders of resistance change. Various GeTe switch design constraints are studied via cross sectioning of the fabricated device using a focused ion beam (FIB)-SEM. Current-carrying capacity and resistance of microheaters are extracted using electrical characterization. The RF performance of the PCM switches is optimized using diverse design parameters and characterization of PCM thin films. A six-layer microfabrication process is presented for monolithically integrating RF circuits with PCM switches. Methods to reduce parasitic elements in PCM switches are discussed. The RF performance of the optimized PCM based switch is simulated and measured demonstrating better than 0.4 dB of insertion loss and a return loss better than 20 dB from dc to 67 GHz.
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) | 0.001 | 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".