JTAG debug tool for efficient debugging on V93K
Bibliographic record
Abstract
The JTAG protocol is used extensively in testing today's complex IC devices. It is used to access DFT structures and many other configuration registers in the device. JTAG sequences are used to configure the device in certain modes before test and to obtain information after test. Test engineers needs to learn DFT structure and how to configure the settings to test the device correctly. This “learning” is usually done in real-time on the tester by trial and error, seeing how the device responds as bits are flipped in the configuration registers. This learning involves developing JTAG patterns for all different configurations the engineer wishes to try. It is a process in which the JTAG patterns can change frequently until the right settings are obtained. The time delay for bringing a new JTAG pattern into the test program can be quite costly if the conversion/compiling from STIL to BINL is long: every change or mistake made in the JTAG pattern can translate into hours. Though there are advanced tester tools such as protocol-aware readily available on the Advantest 93K tester platform (referred as V93K in this article), the existing EDA outputs cannot be leveraged without building registers sequence by sequence. We developed a JTAG debug tool on V93K to address this time delay for bringing a new JTAG pattern into an already-loaded test program. The tool has the ability to copy the STIL content directly into the tester vector memory without any delay for conversion to BINL. The tool also takes care of creating pattern burst dynamically, executing and reporting functional test results. The debugged patterns can be used directly for a production run. This tool also can be applied to any other protocol without modification. This paper details the steps used in developing this tool and explains its functionality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.020 |
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".