Bibliographic record
Abstract
The recently approved IEEE 802.3ae 10 Gigabit Ethernet (10 GE) standard is an economical solution to the existing speed bottlenecks in short reach local and metropolitan area networks. One of the primary challenges in implementing 10 GE is the design of cost-effective, wide dynamic range and low power optical receivers. The dynamic range of an optical receiver depends on the performance of the transimpedance amplifier (TIA). In this work, a high speed TIA suitable for all short to medium reach 10 Gb/s applications, including 10 GE and OC-192 SONET, was implemented in IBM's mature 0.5mum SiGe BiCMOS technology. A new parallel feedback topology employing both shunt-shunt and shunt-series feedback was utilized to achieve wider dynamic range than a conventional common-emitter shunt-shunt feedback design. Given an optical overload specification, the parallel feedback architecture can use a larger feedback resistor resulting in improved noise performance. The fabricated TIA had a total area of 1 x 1 mm2 and featured a dynamic range of 18.4 dBm; over which it maintained a bit error rate (BER) less than 10-12. The power consumption was 189mW from a 3.3V supply. This represents a 55% power savings when compared to previously reported state of the art designs.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".