An Improved Inductance and Self-Resonance Frequency Modeling and Estimation of Single-Turn On-Chip Inductors for Millimeter-Wave Applications
Bibliographic record
Abstract
This article reports accurate, unique, yet simple closed-form expressions for estimating the inductance and self-resonant frequency of single-turn on-chip inductors of different geometries. The proposed expressions are solely dependent on the geometry and the sizing of the inductors without requiring detailed process information. Theoretically estimated results are extensively verified with measurements and electromagnetic (EM) simulations, for i) different geometries, ii) different dimensions of the inductors, and iii) different metal thicknesses. Various single-turn octagonal inductors are designed and implemented using the top metal of a 1-poly 6-metal 180nm CMOS process. The inductors are measured by manual wafer-probing with appropriate calibration and de-embedding techniques. The proposed expression estimates the inductance with an accuracy of 95 % or an error of less than 5% for all inductors ranging from 70 pH to over 250 pH. A new approach based on transmission line theory to model and estimate the self-resonance frequency (SRF) is proposed and a closed-form expression is developed to estimate the SRF of single-turn inductors of different geometries. This expression is easier to use and does not require the knowledge of obscure process-related data. The accuracy of the proposed expression is better than 95% when compared with the EM simulation results.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".