Gate Leakage Current Integration-Based Dielectric Breakdown Monitor in a 12nm FinFET Process
Bibliographic record
Abstract
Time-dependent dielectric breakdown (TDDB) is a critical contributor to wear-out failures in semiconductors, aggravated by scaling of thin-oxide fabrication processes. Stress-induced leakage current (SILC) that increases with wear-out in these thin-oxide transistors correlates to TDDB hard failure risk, however existing in-field monitoring solutions struggle to characterize picoamp currents and maintain effective bias during measurements. We present a sensor for in-field predictive TDDB failure risk assessment of gate dielectrics based on SILC characterization via a current integration measurement scheme. The sensor enables constant voltage bias and current amplification during measurement within a simple 5-transistor topology. Our design is fabricated in a 12nm FinFET process and subjected to accelerated aging stress using an in-house test platform to verify the design, with eight sensor variants showing good correlation with simulation results. Observed SILC degradation and soft breakdown events allow for detailed analysis and comparison of transistor wear-out across gate oxide stack-ups, enabling future semiconductor in-field failure risk management strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".