CMOS Technology Scaling Considerations for Multi-Gbps Optical Receivers With Integrated Photodetectors
Bibliographic record
Abstract
The integration of photodetectors for optical communication into standard nanoscale CMOS process technologies can enable low cost for emerging high volume short-reach parallel optical communication. Whereas past work has highlighted the challenges that face integrated photodetectors in highly scaled CMOS technologies, this work examines the opportunities afforded by these new technologies. First, scaling promises improved extrinsic photodetector bandwidth thanks to improved TIA performance. Second, modern advanced process features enable new photodetector structures with improved performance. A phototransistor employing deep n-wells is characterized in 65-nm CMOS and exhibits a more than ten-fold increase in responsivity over a similar structure without the buried n-well. Third, equalization techniques benefit from technology scaling and are only just beginning to be applied to CMOS integrated photodetectors. In particular, decision feedback equalization appears to offer potential for 10+ Gbps operation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".