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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".