Resolution enhancement in probing of high-speed integrated circuits using dynamic electrostatic force-gradient microscopy
Bibliographic record
Abstract
Dynamic mode electrostatic force microscopy is a technique capable of measuring the internal voltage signals of high-speed integrated circuits. Circuit signals are extracted by utilizing the localized nonlinear Coulomb force acting on a microfabricated probe that is closely positioned above the circuit test point. Equivalent time sampling of repetitive high-frequency signals, that can have a bandwidth much greater than the mechanical response of the probe, is achieved by driving the probe with amplitude-modulated high-speed pulses. Currently, dynamic mode electrostatic force microscopes (DEFMs) extract circuit voltage signals through direct sensing of the electrostatic interaction which results in a poor spatial resolution and is susceptible to interference due to significant coupling to the tip sidewall and the cantilever support of the probe. This is especially true for large tip-to-sample distances such as when passivated circuits are measured. This article describes a force-gradient method to improve the spatial resolution of DEFM. The force-gradient method is implemented numerically and is based on sensing the force as the tip-sample distance is modulated. The method is shown to reduce the contribution from the tip sidewall and the cantilever. Measurements of high-speed signals up to 500 Mb/s demonstrate a significant reduction of interference signals.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".