A digital tester architecture for a system-on-chip implementation.
Bibliographic record
Abstract
This thesis presents the development of an Intellectual Property (IP) core for a System-on-Chip (SoC) implementation of an integrated circuit tester. The resulting realization is called a Tester-on Chip (ToC). The ToC IP core is used in conjunction with a microelectromechanical (MEMS) interface that provides the necessary connectivity between the tester circuitry and the Device Under Test (DUT). Instead of using traditional Automatic Test Equipment (ATE) that includes a complex external test head, the DUT is placed in a MEMS fixture or socket and spring loaded MEMS contacts are used to probe the DUT as required. The ToC implementation can generate and apply a comprehensive set of test vectors at-speed. The resulting test response information is analyzed by the ToC and the corresponding test results are sent via a Universal Serial Bus (USB) interface to a host computer, such as a laptop computer, for visualization and decision making. A scalable vector RAM is used to store the test vectors and it is held as a separate module in the MEMS interface socket. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .R37. Source: Masters Abstracts International, Volume: 42-02, page: 0649. Adviser: W. C. Miller. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".