Database approach for storage and retrieval of test parameters for manufacturability
Bibliographic record
Abstract
This paper deals with the test challenges in transferring test parameters from one insertion to another. The method is elaborated with the context of configuring the correct data to be programmed in the functional fuses of dual core processors. Functional fuses are device family specific and are used to configure thermal offset limits, device specifications and many other vital parameters. The challenge is to transfer the parameters obtained from one automatic test equipment (ATE) insertion to a different one for fusing. The current method stores this data in a portion of the functional fuses reserved for engineering. However, this method limits the amount of data that can be stored as well as the number of fuses available for other purposes. This paper proposes a new method to store the fuse values in a centralized database. The approach is based on querying for a particular part's previous test data stored in the central database and using the data returned to correctly program the fuse bits. This technique is extended to have flow control, segregation of parametric outliers and checksum of fused data to cater for manufacturing.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".