Waiting time optimization of non-deterministic tests at ATE
Bibliographic record
Abstract
One of the important steps that a semiconductor chip goes through is electrical testing, which typically is performed on an automated test equipment (ATE) platform. The primary goal of this step is to achieve maximum test coverage while minimizing testing time as much as optimal. There are various test methods used in testing, ranging from a simple continuity test to implementations such as built-in self-tests (BISTs) in which the chip is instructed to run internally and the test program checks for the result only when it is done. BISTs are, in a way, non-deterministic in nature; testing time can vary depending on the internal clock frequency at which the test is run and other factors based on the chip's operation. One way to optimize testing time of non-deterministic tests at ATE is to read the results register immediately on completion of the BIST execution, and not wait for a hard-coded amount of time to elapse - typically, an amount of time based on the slowest possible test execution to complete. This paper discusses a method that eliminates unnecessary time lost waiting for data that may have long arrived.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".