A probabilistic test instrument using sigma-delta phase signal generation technique for mixed signal embedded test
Bibliographic record
Abstract
A probabilistic test instrument is proposed for mixed-signal embedded test applications. The system architecture of the instrument and its implementation is presented. The instrument can be used to inject and extract the timing and voltage information associated with signals in high-speed transceiver circuits that are commonly found in data communication applications. Using statistical methods, the probability distributions associated with these signals can be extracted using a simple circuit called a probability extraction unit, consisting of a few simple digital logic gates. At the core of this work is the use of ΣΔ phase-encoding technique to generate both the voltage and timing (phase) references, or strobes used for high-speed sampling. This technique is also used for generating the test stimulant for the device-under-test, or DUT as a shorthand notation. Experimental results reveal the sampling time strobe can be programmed over a phase range of 45 degrees with a phase step of 1 degree at a fixed voltage reference. The DUT stimulant and the timing and voltage references are all programmable in software. This provides additional flexibility and versatility when conducting a test. A prototype of the proposed test instrument was implemented using discrete components assembled on a printed-circuit board and shown to be capable of measuring the output jitter distribution associated with a clock and data signal of a DUT. It was further extended to measure the phase and frequency response of various analog channels associated with the DUT. The performance of the instrument was evaluated by comparing the test results with those obtain using other test techniques, independent of the instrument.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".