A Receiver Front-End for VCSEL-Based Optical Links With 49 UI Turn-On Time
Bibliographic record
Abstract
A fast turn-on front-end (FE) for a burst-mode nonreturn to zero (NRZ) receiver targeting vertical cavity surface-emitting laser (VCSEL)-based optical links operating at 10 Gb/s/ch is presented. Its bandwidth and power are reconfigurable for energy-efficient burst-mode operation. The circuit design for 4.9 ns power-ON time using a power-gating approach is presented. Rapid power-ON is achieved using the regeneration of a high-speed latch, activated by a quarter-rate clock. Implemented in a 65-nm CMOS technology, the proposed FE consists of a shunt-feedback transimpedance amplifier (TIA), a configurable one-stage or four-stage postamplifier (PA) and an offset compensation loop. By reconfiguring the number of stages in the PA from four to one, power dissipation, and the FE bandwidth are reduced during periods of link inactivity while still maintaining sufficient gain to allow the detection of an incoming burst. The presented work is supported by simulation and measurement results with optical inputs at 10 Gb/s. The results demonstrate a 4.9-ns turn-on time, corresponding to 49 unit intervals (UIs). The overall FE dissipates 5.7 mW at 10 Gb/s (0.57 pJ/bit) and 1.9 mW during idle periods. The complete receiver of each channel occupies an area of 95$\mu \text{m}\,\,\times81\,\,\mu \text{m}$. A 5% area overhead is introduced by the burst sensing circuit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.003 |
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".