Creating screen‐printed passive components for microwave applications
Bibliographic record
Abstract
Purpose The purpose of this paper is to share valuable information about low‐cost microwave circuit research with academic and industrial communities that work, or want to work, in this field. Design/methodology/approach Screen‐printing technology has been chosen as the fabrication method because of simplicity and low costs. Different materials and printing parameters were tested in four generations of microstrip lines. After obtaining a satisfactory fabrication method, passive microwave components were printed, assembled, characterized and modeled. Findings Results demonstrated that the proposed low‐cost method allows fabricating low loss microstrip lines (15.63×10 −3 dB/mm at 10 GHz), filters, inductors, and capacitors that work well up to 12 GHz. Research limitations/implications Model accuracy of inductors and capacitors can be improved. The use of more precise calibration and de‐embedding techniques is necessary. More components can be fabricated and modeled to increase the flexibility and applicability of the proposed fabrication method. Practical implications The presented information can help limited budget companies and small educational institutions in electronics to fabricate microwave circuits at low costs. This is an excellent approach for students who want to learn how to make microwave frequency measurements and circuits without the need of expensive fabrication equipment and clean rooms. Originality/value The step‐by‐step fabrication method described in this paper allows fabricating different microwave components at low costs. The presentation of electrical models for each component completes the design‐fabrication cycle. As this information is gathered in a single source, it makes easier the incursion of new actors in the microwave field.
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 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 | 0.000 | 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) | 0.000 | 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 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".