Quarter-Rate Bang-Bang 위상검출기를 사용한 0.18μm CMOS 10Gbps CDR 회로 설계
Bibliographic record
Abstract
통신시스템에서 데이터 전송이 고속으로 이루어지면서, 하드웨어의 복잡성, 전력소모, 가격 등의 이유로 클럭을 제외한 데이터만 수신단으로 보내는 방식이 사용되어지고 있다. 따라서, 고속으로 수신된 데이터에서 클럭 신호를 추출하는 것이 필요하며, 추출된 클럭을 이용하여 데이터를 복원하는 클럭/데이터 복원회로(CDR)에 관한 연구가 활발히 이루어지고 있다. 본 논문에서는 0.18um CMOS 공정을 이용하여 10Gbps CDR 회로를 설계하였다. 전력소모와 회로의 복잡도를 줄이기 위해 quarter-rate bang-bang 유형의 위상 검출기를 사용하였으며, 지터 특성 향상을 위해 LC 유형의 4단 VCO를 사용하였다. 모의실험 결과, 설계된 CDR 회로는 1.8V 전원전압에서 80mW의 전력을 소모하며, 2.2ps,pp의 클럭 지터 특성을 보인다. 패드를 제외한 칩 레이아웃 면적은 1.26mm×1.05mm이다.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".