A Single-TSV and Single-DCDL Approach for Skew Compensation of Multi-Dies Clock Synchronization in 3-D-ICs
Bibliographic record
Abstract
Existing methods used for the clock distribution of multiple dies employ a balanced tree structure to minimize the impact of the within-die process and loading variations. No topology for die-to-die clock skew compensation of more than two dies has been presented yet. This article presents a novel die-to-die clock skew compensation topology to address these limitations. Unlike existing designs, the proposed topology does not need a phase detector (hence, no dead zone); it only requires one through-silicon via (TSV) to connect a pair of dies and one digitally controlled delay line (DCDL) in each die; thus, there is no skew from extra TSVs and DCDLs. Accordingly, the system has a small chip area and low lock time. The postsynthesis of this work was accomplished in a 65-nm CMOS process. The performance of our design was evaluated theoretically and practically in terms of mismatch/finite resolution of delay lines, buffer mismatch, and TSV delay. Under identical conditions, the residual skew of the proposed design was as low as 13 ps at 1 GHz. This study is the first to obtain the solution for die-to-die clock synchronization of multiple dies (more than two dies) in a three-dimensional (3-D) integrated circuit, while other systems can only support two dies.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".