An Area Efficient and Inductorless Implementation of Continuous-Time Linear Equalization Scheme for High Speed and Low Noise TIA Designs
Bibliographic record
Abstract
This paper presents an inductorless fully differential and linear design of continuous time linear equalization (CTLE) to simultaneously optimize the bandwidth and noise of transimpedance amplifiers. The proposed CTLE is implemented with RC components in a negative feedback configuration. The proposed design is compared with the conventional CTLE approach that uses an inductor-based implementation. The comparison suggests that the proposed CTLE not only achieves the same equalized bandwidth but also results in less group delay variations as compared to its inductor-based counterpart. Additionally, a TIA is designed using the proposed CTLE approach in GF-BiCMOS 90 nm$(\mathbf{f}_{\mathrm{t}}=\boldsymbol{310}$GHz) process and its performance is verified by post-layout simulations which include the loading effects of photodiode and$50-\Omega$output loads. As per the authors' best knowledge, the proposed TIA design is the first of its kind in the sense that without the use of inductors in any stage, it can support up to 80 Gb/s NRZ data stream with a transimpedance gain of 72$\mathbf{dB}-\Omega$and an input-referred noise density of about 1.25 pA/sqrt(Hz).
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".