Session 14: Characterization, reliability, and yield - ESD/memory reliability
Bibliographic record
Abstract
The ESD and non-volatile memory reliability session focuses on recent developments in ESD and Latchup protection methodology and reliability challenges for NVMs. The invited talk on the ESD qualification for 45 nm and beyond provides first hand information about the vivid discussion on ESD target levels started in the electronics industry. For 22 nm and beyond FINFET technology might become an option which requires an appropriate ESD protection as discussed by the second paper. The growing awareness of a latchup optimized design is addressed by the study of guard ring interactions and their effect on CMOS Latchup resilience. In the second part of the session, Non-Volatile Memory reliability aspects will be presented, starting with a new method to evaluate the trapped charge distributions in SONOS-type devices. Retention implications will be discussed in this presentation, as well as in the following one, focused on the statistical investigation of leakage current in floating gate cells with a high-k interpoly dielectric. In the last paper of this session, evidence will be provided for bit flips produced in Flash memories by the atmospheric neutrons deriving as ground level byproducts of cosmic ions.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.015 |
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".