Testing for floating gates defects in CMOS circuits
Bibliographic record
Abstract
This thesis studies the detectability of MOS floating gate transistor faults considering classical Static Voltage, Dynamic Voltage and Static Current testing strategies. The behavior of the defect depends on two classes of parameters: the predictable and unpredictable parameters. A floating gate fault can induce abnormal logic values, additional delays, or increased power supply current. Consequently, classical test strategies can only detect floating gate faults for a given range of the unpredictable parameter. Here, a new test scheme is proposed, which allows a considerable current to flow in the faulty logic gate in stable state, making the circuit with a floating gate IDDQ testable. It is shown that a combination of voltage and current testing can ensure complete detection of the floating gate defects, i.e., regardless of the unpredictable parameters. Analysis with increasing initial charge on the floating gate transistor shows how the detectability intervals become smaller for the voltage testing strategies and increase for the static current strategy. Keywords: Floating gate testing, IDDQ testing, gate opens, floating gate defect model.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".