A 24-GHz DCO With High-Amplitude Stabilization and Enhanced Startup Time for Automotive Radar
Bibliographic record
Abstract
In this paper, the optimized design strategies for the implementation of a CMOS digitally controlled oscillator (DCO) are investigated. Moreover, the boosting mechanism for a DCO with and without negative resistance is considered. The proposed design methodology is based on an in-depth mathematical analysis of the startup condition and amplitude of oscillation. This approach results in an optimized topology for a Colpitts Clapp-DCO (CC-DCO). The improved performance is achieved through the negative resistance boosting mechanism. The negative resistance enhances the startup time and increases amplitude stabilization across a wide tuning range (TR). Moreover, it improves the phase noise (PN) performance while suppresses the amplitude-to-phase conversion. The proposed 24-GHz CMOS enhanced CC-DCO (ECC-DCO) is implemented in 65-nm TSMC CMOS process. It can effectively reduce the startup time by 41%. Also, it boosts and stabilizes the amplitude across a TR of 29%. The amplitude varies by 1.5% across the 22-29-GHz TR. The ECC-DCO consumes 12.8 mW. It shows a PN of -106 dBc/Hz at 1-MHz offset frequency and achieves -185-dBc/Hz figure of merit (FoM) and -194-dBc/Hz FoM for tuning.
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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) | 0.001 | 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".