Inter-Signal Timing Skew Compensation Of Source-Synchronized Parallel Links With Incremental Signaling
Bibliographic record
Abstract
This thesis deals with inter-signal timing skew compensation of source-synchronized multi-Gbytes/s parallel links with both voltage-mode and current-mode incremental signaling schemes. To compensate for the inter-signal timing skew of parallel links with voltage-mode incremental signaling, an early/late block that detects the rising and falling edges of the pulses generated by inter-signal timing skews at the far end of the channels, and subsequently allocates the optimal sampling point of the sampler of each data bit to maximize the timing margins. Two cascaded delay-locked loops are employed to place the sampling clock to the optimal sampling position of each data bit. To compensate for the inter-signal timing skew of parallel links with current-mode incremental signaling, each current-mode receiver maps the direction of its channel current representing the logic state of the incoming data to two voltages of different values. The feedback at the front-end of the receiver minimizes the dependence of the input imedance of the receiver on the channel current so that data dependent impedance mismatch is minimized. Inter-signal timing skews are compensated by inserting a delay line in each channel.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".