Progress and trends in multi-Gbps optical receivers with CMOS integrated photodetectors
Bibliographic record
Abstract
There has been significant recent progress towards the realization of multi-Gbps optical receivers fully integrated into standard CMOS processes. Although CMOS photodetectors exhibit performance inferior to discrete photodetectors, they offer the potential for a low-cost highly-integrated solution that suits growing and emerging applications in short-reach optical communication. Past work has focused on using the pn-junctions and depletion regions available in standard CMOS process flows to eliminate, minimize, or cancel the slowly diffusing photocarriers that usually limit the bandwidth of CMOS photodetectors. However, if considered simply as a form of ISI, the slowly diffusing carriers can be dealt with using the same signal processing tools in wide use for other wireline communication applications, including decision feedback equalization. A combination of spatially-modulated light detection, analog equalization, and modest decision feedback equalization appears to offer a path towards data rates in excess of 10-Gbps using integrated photodetectors. Nanoscale CMOS is particularly well suited to the implementation of such signal processing functions. Measured results of photodetectors implemented in a standard 65-nm CMOS process are presented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".