Bibliographic record
Abstract
PAM4 modulation such as PCIE6 link is prone to voltage noises due to reduction on signal to noise ratio to 9.6dB relative to NRZ signaling. One of the biggest contributors to voltage degradation is due to signal reflection at interconnect and mode-conversion cause by imbalanced in channel properties such as impedance variations and delay. This paper investigates intra-pair skew due to cable bending empirically using lab measurements. Firstly, the main concern for cable skew is on common-to-differential mode conversion since it will add as noise at differential receiver. Our studies indicated a small increase of Sdc21by 3dB for different bending cases compared to baseline thus alleviated the concerns of increase Sdc21 cause by cable bending. Secondly, excessive cable bending can cause damage or change of electrical properties such as characteristic impedance and propagation delay. Our studies indicated cable bending cause small impedance discontinuities of 1 to 2-ohm at locality of the bending. This studies also suggest cable impedance variations at location of bending to be modelled into end-end simulation. We also observed negligible differential propagation delay variations of <1 ps for all four cases. Effective intra-pair skew (EIPS) is also <3ps which is within PCIe Gen6 cable requirements. Simulations suggested negligible impact to margins since the cable impedance and skew variations are still within the expected high-volume manufacturing (HVM) variations and tolerances. These studies suggest there are possibility to bend cable beyond cable manufacture recommendation in a very tight system yet still passing the performance requirement. However, it is advice for system architect to work closely with cable vendor on mechanical and reliability risks. Cable vendor may have options to recommend a different option such as cable jackets reinforcement or other options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".