Recent advances in fault isolation for semiconductor industry
Bibliographic record
Abstract
In semiconductor companies, failure analysis (FA) activities play a major role in all many areas. FA is deeply involved in new process technology development, 1st Silicon bring-up, wafer sort and backend yield improvement, product qualification and customer return analysis. RegardLeSS Of area that FA SUPPOrtS, there IS aLWaYS a need fOr faULt ISOLatIOn PrIOr tO the PhYSICaL Or deStrUCtIVe faILUre anaLYSIS. FaULt ISOLatIOn IS the SteP Where We narrOW dOWn the area Of a faILIng Part Or PrOdUCt tO a manageabLe area. ThIS aLLOWS FA engIneer tO ImPrOVe the SUCCeSS Of PhYSICaLLY fIndIng rOOt CaUSe Of the faILUre and SPeedS UP the TUrn-arOUnd tIme fOr the anaLYSIS. ThIS InVIted taLK WILL COVer reCent adVanCeS In faULt ISOLatIOn teChnIqUeS and tOOLS In deVICe/SILICOn and PaCKagIng. CaSe StUdIeS fOr thOSe teChnIqUeS WILL be COVered tO PrOVIde greater UnderStandIng fOr the teChnIqUeS tO the aUdIenCe.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".