Transformer-Based Machine Learning Model for Power Plane Defect Localization and Higher-Order Electromagnetic Behavior Analysis in 3D IC Structures
Bibliographic record
Abstract
This paper demonstrates a transformer-based power plane defect localization model utilizing the electromagnetic return loss behaviors of defective chips. This is the first application of a transformer-based machine-learning approach to power plane defect localization in 3D integrated circuits. The model effectively learns the chip structure through the frequencydomain return loss data, enabling accurate defect localization. Key innovations include a composite input matrix combining defective and defect-free scenarios, a multi-head self-attention mechanism for feature extraction, and a custom loss function integrating matrix-level and row/column-specific accuracy enhancements. By simultaneously performing defect localization tasks and understanding the underlying chip structure using the electric field approach, the proposed method offers a dual advantage: precise localization of defects and insight into the structural properties of the chip. This approach highlights the potential of transformer-based models to advance failure analysis and enhance chip design and reliability in next-generation integrated circuits.
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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) | 0.000 | 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) | 0.001 | 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".