An electrical-aware parametric DFM solution for analog circuits
Bibliographic record
Abstract
Today, many of the approaches that are commonly referred to as physical DFM techniques only address catastrophic defects and systematic process variations. These techniques include spreading wires, doubling vias, identification of critical areas in the circuit that are especially susceptible to defects, and identification of proximity effects caused by the lithography process. However, physical DFM tools are purely “geometric”, in that they work to preserve shape fidelity without any knowledge of the impact on the electrical characteristics of the shapes that are manufactured in silicon. While these techniques have proven useful in reducing functional failures and increasing overall yield by a few percentage points, they completely ignore the more important category of parametric failures. The proposed solution presented in this chapter specifically helps to address the parametric performance modeling problems encountered at smaller geometries. As this solution drives design requirements into physical layout design and moves layout awareness upstream into design, useful information about the design (on the physical and electrical level) is captured, analyzed, and simulated. Deviations in the electrical characteristics due to physical layout and process variations, are identified and highlighted on the design. These deviations are referred as electrical hotspots (e-hotspots). To validate this work, The proposed e-hotspot detection engine is verified against silicon wafer data for a level shifter circuit designed at 130nm. The e-hotspot devices with high variation in DC current and causing parametric failure, are identified.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".