Quantitative Capacitance Measurements of MOS Structures using a Scanning Probe Microscope
Bibliographic record
Abstract
This paper describes the simulation and implementation of a method by which an atomic force microscope with scanning capacitance microscopy (SCM) capability can be employed in a nontraditional fashion to quantitatively measure the capacitance of metal-oxide-semiconductor (MOS) structures. The capability to deduce sample capacitances is based on resonant frequency shifting, which relies on the SCM's ultra-precise capacitance sensor. The technique, however, is distinct from scanning capacitance microscopy imaging, with the MOS capacitor an integral part of the system resonant circuit. SPICE simulations are performed to extract phenomenological resonant circuit parameters specific to the instrumentation, subsequently permitting sample capacitance to be quantitatively extracted from the system response. Our technique represents a novel application of SCM instrumentation and has important applications in the analysis of on-chip passive components for future technology generations. Initial experimental results are promising, suggesting the extension of the technique to advanced technology nodes
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".