TIME-INTERLEAVED MIXED-SIGNAL TEST CORE DIGITIZERS
Bibliographic record
Abstract
System-on-Chip (SoC) is one of the main driving forces that have been re-shaping the consumer electronics industry. The SoC alternative to conventional systems design is growing in popularity as the device packing densi escalates due to the evolution of semiconductor technology. Moreover, the decrease in semiconductor feature size is permitting the increase of clock frequencies and component operating speed. These advancements necessitate the integration of system components due to package parasitics and lengthy interconnect. Furthermore, SoC devices offer a cheaper and more compact solution to the consumer electronics industry.An integrated mixed-signal test core approach to SoC data acquisition is a valued alternate solution to many of the problems involving conventional test. One such test core data conversion architecture incorporates a sub-sampling algorithm known as the multipass method of digitization. This method occupies a very small silicon area in exchange for an increased data conversion time. Time-interleaved test core digitizers provide the capability to reduce test time, increase sampling frequency and increase signal bandwidth. As an implicit result of this compact circuit and multipass processing methodology, significant reductions in noise and spurious tones are observed.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".