6.2 A 60Gb/s PAM-4 ADC-DSP Transceiver in 7nm CMOS with SNR-Based Adaptive Power Scaling Achieving 6.9pJ/b at 32dB Loss
Bibliographic record
Abstract
With the introduction of PAM-4 signaling at 56Gb/s and the decreased benefits of CMOS scaling for high-speed mixed-signal designs, SerDes designers and system architects are faced with severe performance versus power budget constraints. Power management and energy efficiency have become the main drivers for system design. However, industry standards such as EEE have failed to keep up with efficiency demands. In this context the choice between a so-called analog mixed signal (AMS) SerDes architecture vs. an ADC-DSP-based one has been debated at length. AMS provides significantly lower maximum power [2, 4] while ADC-DSP provides higher link margin [1] thus avoiding expensive and power hungry repeater ICs that largely negate the power advantage of AMS SerDes in a system. AMS provides an easier and cheaper approach to implement multi-tap DFEs [3] compared to DSP where it is typically very expensive to implement more than a 1-tap DFE. This paper will show an ADC-DSP SerDes transceiver with a 2-tap DFE is capable of operating error-free over a 38dB link yet having an overall power budget similar to AMS. The same basic SerDes architecture is implemented (Fig. 6.2.1) with minor differences in 16nm and 7nm FinFET, however, power scaling is incorporated into the 7nm version only.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".