A circuit design and fabrication approach to address global process variation
Bibliographic record
Abstract
This paper proposes a new approach for reducing the consequences of global process variation and improving integrated circuit yield. In the proposed technique, a design of a given circuit block is optimized for multiple process corners, giving rise to multiple sub-designs. The sub-designs are constructed such that all can be implemented using the same front-end-of-the-line mask steps, and having back-end-of-the-line processing differing by as few as one mask step (e.g., the Via1 layer). During fabrication, in-line measurements made after the first level of metal deposition determine which sub-design is fabricated through the appropriate selection of the mask step variant. The technique allows for per-wafer or per-reticle circuit customization based on the wafer's or reticle's process parameters. A tapered buffer chain is investigated as an example of the technique. Simulation results show yield improvements of up to 20% and reductions in power dissipation up to 18%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".