A 20 Gb/s CMOS Optical Receiver With Limited-Bandwidth Front End and Local Feedback IIR-DFE
Bibliographic record
Abstract
Implementation of highly integrated optical receivers in CMOS promises low cost, but combining high gain, low noise, high bandwidth, and low power in a CMOS transimpedance amplifier is a challenge. Fortunately, the sensitivity of an optical receiver is improved by limiting its frontend bandwidth far below the symbol rate and using equalization to eliminate the resulting intersymbol interference (ISI). Analysis reveals that when using a decision-feedback equalizer (DFE) to cancel all postcursor ISI, receiver sensitivity is optimized by taking a front-end bandwidth as low as 0.12 fbit, depending upon the frequency response and noise spectrum assumed for the front end. This paper presents a 20 Gb/s optical receiver with a front-end bandwidth of 3 GHz. The front end is designed to have an approximately first-order response, ensuring only postcursor ISI, which may be efficiently canceled with a first-order infinite-impulse response DFE (IIR-DFE). An IIR-DFE circuit is also proposed that obviates the need for an explicit full-rate multiplexor. Fabricated in 65 nm CMOS, the receiver achieves 0.705 pJ/b efficiency with the IIR-DFE consuming 150 fJ/b. Using a photodiode with 12 GHz analog bandwidth and responsivity of 0.5 A/W, the receiver has a sensitivity of -5.8 dBm optically modulated amplitude.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".