CMOS Reliability From Past to Future: A Survey of Requirements, Trends, and Prediction Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Developments in IC fabrication, emerging high-reliability markets, and government regulations indicate potential for significant shifts in how reliability fits within IC development and product life-cycles. This survey takes a comprehensive look at trends in IC reliability and investigates the methods used to predict failures. A background overview of recent and expected advances in IC fabrication is provided, along with reliability requirements for different markets and review of key aging mechanisms affecting modern ICs. The survey of reliability trends captures the body of research examining degradation across process nodes, changes in transistor architecture, and changes to device materials. High-level analysis of conclusions reveals significant uncertainty with regards to many changes and a diverse range of topics warranting further research. A critical look at current reliability prediction methods used to characterize product reliability is followed by a survey of research developing novel prediction methods to enhance and improve on existing techniques. These topics come together to illustrate the state of IC reliability characterization and potential paths to overcome upcoming challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it