A reconfigurable digital IC tester implemented using the ARM Integrator rapid prototyping system
Bibliographic record
Abstract
We describe the implementation of a low-cost, highly-reconfigurable digital integrated circuit (IC) tester using the ARM Integrator ARM7TDMI/sup /spl reg//-based rapid prototyping system (RPS). The IC tester is controlled through a user interface application that runs on the RPS personal computer (PC) host. Test vectors and expected response vectors are developed off-line and then downloaded via the PC into the tester's pattern memory, which; is implemented in the RPS's SDRAM. The test vectors are then applied at up to 20 MHz to the inputs of the device under test (DUT) using no-return-to-zero (NRZ) formatting; meanwhile, the DUT output signals are sampled mid-cycle and the resulting actual response vectors are compared against stored-expected responses to determine if the DUT is functioning properly. A Xilinx Virtex-E field-programmable gate array (FPGA) in the RPS is used to implement the pipeline that demultiplexes and formats the test vectors, compares actual and expected response vectors, and collects failure statistics. The FPGA is also used to implement the expected response memory. When the test has finished, the actual responses and comparison results can be uploaded from the FPGA RAM to the PC and stored in a text file.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".