Infrastructure for the design and fabrication of MEMS for RF/microwave and millimeter wave applications
Bibliographic record
Abstract
This article describes and provides valuable information for companies and universities with strategies to start fabricating MEMS for RF/Microwave and millimeter wave applications. The present work shows the infrastructure developed for RF/Microwave and millimeter wave MEMS platforms, which helps the identification, evaluation and selection of design tools and fabrication foundries taking into account packaging and testing. The selected and implemented simple infrastructure models, based on surface and bulk micromachining, yield inexpensive and innovative approaches for distributed choices of MEMS operating tools. With different educational or industrial institution needs, these models may be modified for specific resource changes using a careful analyzed iteration process. The inputs of the project are evaluation selection criteria and information sources such as financial, technical, availability, accessibility, simplicity, versatility and practical considerations. The outputs of the project are the selection of different MEMS design tools or software (solid modeling, electrostatic/electromagnetic and others, compatible with existing standard RF/Microwave design tools) and different MEMS manufacturing foundries. Typical RF/Microwave and millimeter wave MEMS solutions are introduced on the platform during the evaluation and development phases of the project for the validation of realistic results and operational decision making choices. The encountered challenges during the investigation and the development steps are identified and the dynamic behavior of the infrastructure is emphasized. The inputs (resources) and the outputs (demonstrated solutions) are presented in tables and flow chart mode diagrams.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".