On the Analysis and the Mitigation of Power Supply Noise and Power Distribution Network Impedance Variation for Scan-Based Delay Testing Techniques
Bibliographic record
Abstract
In this paper, we analyze the impact of the power supply noise and the power distribution network (PDN) impedance variation on the timing margin in both modes for ICs with multiple clock domains. We investigate the so-called intermodulation products (IMPs). We show that IMPs are mainly induced by the dependent nature of the transistors. We also provide experimental results showing that scan-based delay testing can be optimistic with respect to the mission mode for maximum achievable nominal frequency prediction, even at lower clock frequencies. We also show that IMPs can induce timing margin fluctuations that can be larger than that of the ones induced by the voltage droop in the test mode. Using an improved HSpice simulation model of a PDN validated by experimental results, we also quantify the timing margin variation due to power noise in the test mode as a function of the clock frequency, including the so-called clock stretching phenomenon. Finally, we propose a robust test signal scheme for multiple clock domain chips. The simulation results reveal that this scheme is less sensitive to PDN impedance variation than that of the most popular existing test schemes, and that it provides timing margins closer to those obtained in the mission mode.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".