Accurate Modelling of Thin-Film Resistor up to 40 GHz
Bibliographic record
Abstract
We present an accurate scalable model for thin-film resistor from DC up to 40GHz. The model is based on the microstrip theory, and an empirical self-capacitance parameter is introduced. The simulated S-parameters of the model show very good agreement with the measurements. Thin-film resistors (TFRs) are used in microwave circuits to implement components such as passive attenuators and terminal loads. To date, TFR model considers the parasitic series inductance and shunt capacitance to remain the same from the lossless microstrip case [1]. However, this model does not provide good results when the width of resistor is much smaller than substrate thickness. In this work, the self-capacitance is taken into account to make the model more accurate. The self-capacitance of planar resistor was first introduced by Demurie [2]. If a voltage is applied at the terminals of a resistor, a potential value will across two arbitrary points A and B in the resistor. Therefore, a parasitic capacitance exists between A and B. In MIC technology, a thin-film resistor is realized as a thin strip of lossy conductor on top of the dielectric substrate. The resistive layer is a self-passivating Tantalum Nitride (TaN) compound. The sheet resistivity of the process can be adjusted by controlling the thickness of the resistive layer. In most processes, a sheet resistivity of 50Ω per square is selected for the convenience it provides designers. A small area of conductor metal is deposited at the ends of the element as the contact to the resistors. The exposed resistive area defines the resistance of the structure. The fabrication design rules generally require that resistors are narrower than the width of the conductor contact by some minimum distance because of process alignment tolerances and the need to have a good contact with conductor layer at the end of resistor.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".