Design of a CMOS 65‐nm inductor‐less VCO for ISM applications in the VHF band
Bibliographic record
Abstract
Summary This paper presents a design of a CMOS cross‐coupled voltage‐controlled oscillator (VCO) using active inductors (AIs) for wide‐band applications and can also be applied to various wireless technologies standards. The compatibility of this design to different wireless standards highlights its potential to be implemented at the core of the communication front end in the Internet of Things (IoT). The proposed AI design employs a gyrator‐C topology as the basic structure to generate an inductance. The VCO uses a cross‐coupled oscillator structure with a pair of varactors to sweep the frequency. Two extra capacitors, between the AIs and the outputs of the VCO core tank, are employed to enhance the performance of the phase noise and make the VCO work similarly to a linear transconductance (LiT) oscillator. Both the AIs and the VCO are designed in the TSMC 65‐nm CMOS technology, and the performance is analyzed using postsimulation results, as well as through measurements. The fundamental frequency spans from 140 to 463 MHz. Thus, the relative tuning range of this design is approximately 107%. The optimal phase noise of the design is around −97 dBc/Hz at 1‐MHz offset. Furthermore, it achieves an excellent figure of merit (FOM) around −163 dBc/Hz with a direct current (DC) power consumption less than 3 mW. The proposed design shows an advantage in phase noise and power consumption in comparison with previous active inductor VCO and ring VCO designs, respectively. The final layout occupies only 0.4 × 0.62 mm2 including the pads. The proposed AI‐VCO shows a compact size, linear tuning, low power consumption, and good phase noise performance.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".