Electrical scanning probe microscopy of electronic and photonic devices: connecting internal mechanisms with external measures
Bibliographic record
Abstract
Abstract The inner workings of semiconductor electronic and photonic devices, such as dopants, free charge carriers, electric potential, and electric field, are playing a crucial role in the function and performance of the devices. Electrical scanning probe microscopy (SPM) techniques have been developed and deployed to measure, with nanometric spatial resolution and high quantitative accuracy, the two-dimensional profiles of dopant, potential, electric field, and free carrier distribution, within unbiased and/or operating electronic and photonic devices. In this review paper, we summarize our latest SPM experimental results, including the scanning spreading resistance microscopy and scanning capacitance microscopy of terahertz quantum cascade lasers, scanning capacitance microscopy of non-volatile memory devices, scanning voltage microscopy of terahertz quantum cascade lasers, and scanning voltage microscopy of interband cascade lasers. Interpretation of the measured quantities are presented and calibrated, demonstrating that important internal physical quantities and inner mechanisms of device operation can be uncovered. It reveals that the novel SPM techniques would find more applications to the emerging semiconductor quantum devices and nanoelectronics.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".