Receiver operational yield in optoelectronic-VLSI applications
Bibliographic record
Abstract
It is well understood to be more difficult to operate an array of receivers simultaneously than individually, as sensitivity is degraded in the presence of simultaneous switching noise.1,2 In optoelectronic-VLSI applications, additional operability concerns exist due to the need to implement receiver circuits of reduced complexity due to physical space constraints and to bias and control receivers in groups. Operational yield refers to the percentage of receivers in a group that can simultaneously be operated successfully. Receivers in a group may be functional individually, but some may exhibit operational problems such as duty cycle distortion or stuck-at 1/0 behavior when operated as a group. The transfer characteristics of optically single-ended receivers can be sensitive to changes in biasing and control parameters. If a sensitive parameter is common to a group of receivers, operational yield can be compromised by problems caused by process variations in optoelectronic devices and in transmitter and receiver circuits, and non-uniformity in optical system power throughput. We present experimental and simulation-based analyses of operational yield for optically single-ended receivers in common bias and control groups. Architectures employing optically differential signaling are shown to facilitate approaches to alleviating operational yield problems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".