8.7 A 112Gb/s ADC-DSP-Based PAM-4 Transceiver for Long-Reach Applications with >40dB Channel Loss in 7nm FinFET
Bibliographic record
Abstract
Driven by the proliferation of rich media services and a drastic increase of data availability, the demand for high-speed data transfer in the data center continues to grow at greater than 26 percent year-over-year [1]. This urges the imminent solution of top-of-rack switches in hyperscale networks with faster I/O interfaces to simultaneously support both low power and high throughput. Supporting the substantial bandwidth increase has driven the development of new electrical and optical interconnect standards which enable 100Gb/s per channel including IEEE 802.3ck and CEI-112G with PAM-4 modulation in conjunction with forward error correction (FEC) [2]. For long-reach applications, a transceiver architecture with >40dB channel equalization is critical due to the extra 8-10dB package insertion loss. To resolve those bottlenecks, this work presents an ADC-DSP based PAM-4 transceiver capable of equalizing >41.5dB lossy channels and achieving 112Gb/s per channel and 896Gb/s overall retimer throughput in 7nm FinFET.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".