Reducing test time for selective populations in semiconductor manufacturing
Bibliographic record
Abstract
As the semiconductor industry prepares for the Internet of Things, one of the major challenges it will face is to maintain quality levels as the volume of devices continues to grow.Semiconductor devices are moving from items of convenience (PCs) to necessity (smartphones) to mission-critical (autonomous automobiles).One aspect of manufacturing operations that can, and must change, in the face of ever-tightening quality requirements is how to test the devices that are shipped into the end market more efficiently while maintaining very high levels of quality.One of the ways to achieve these diametrically opposed goals is through the use of Big Data analytics.Semiconductor manufacturing test today is a 'one size fits all' process, with every device being made to go through the same battery of tests.Devices that initially do not pass are retested to be sure they are not bad, but what about the devices that are 'exceptionally good'?Testing devices that are so 'tight' in their tolerances that statistically they will easily pass any remaining test intended to catch marginal devices is a waste of time and manufacturing resources.Using Big Data analytics within a manufacturing environment can enable companies to establish a 'Quality Index' where every individual device can be 'scored' independently.If that device achieves a high-enough quality score, it can be 'excused' from any further testing to accelerate overall manufacturing throughput with zero impact on quality.This paper will show how semiconductor companies today are putting Big Data solutions in place to improve overall product quality and simultaneously reducing their manufacturing costs by using data they already have in their possession.
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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 | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".