Vector voltage measurement of MMICs using Electrostatic Force Microscopy
Bibliographic record
Abstract
As development in MMIC technology increases, the requirement for high frequency internal node measurement becomes increasingly important. MMIC based systems are becoming more complex with larger densities and smaller devices which demand probing instruments with high spatial and temporal resolution. Proper fault detection requires the ability to perform arbitrary internal node testing. Current probing techniques include: matched impedance[1], electro-optic[2], electron beam[3] and photoconductive sampling[4]. High frequency microwave measurements can be made using matched impedance probes. These allow for high frequency characterization of input and output characteristics of microwave circuits. Unfortunately they provide very little information concerning internal point of failure. The matched impedance probe is much too invasive to use at arbitrary nodes. Internal node probing techniques like contact probing, electro-optic, electron beam and photoconductive sampling are capable of internal node measurement. Scanning force probing techniques are becoming more popular since they rely on the non-contact interaction between a small probe and sample. They employ small micromachined probes with sharp tips (30 nm radius of curvature) which provide ample spatial resolution to probe internal MMIC components. The non-contact nature of this technique also allows for measurement of passivated circuits without the need for predefined test points. These probes typically add less than 1 fF of loading capacitance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".