Electrical Scanning Probe Microscopy: Investigating the Inner Workings of Electronic and Optoelectronic Devices
Bibliographic record
Abstract
Semiconductor electronic and optoelectronic devices such as transistors, lasers, modulators, and detectors are critical to the contemporary computing and communications infrastructure. These devices have been optimized for efficiency in power consumption and speed of response. There are gaps in the detailed understanding of the internal operation of these devices. Experimental electrical and optical methods have allowed comprehensive elaboration of input–output characteristics, but do not give spatially resolved information about currents, carriers, and potentials on the nanometer scale relevant to quantum heterostructure device operation. In response, electrical scanning probe techniques have been developed and deployed to observe experimentally, with nanometric spatial resolution, two-dimensional profiles of the electrical resistance, capacitance, potential, and free carrier distribution, within actively driven devices. Experimental configurations for the most prevalent electrical probing techniques based on atomic force microscopy are illustrated with considerations for practical implementation. Interpretation of the measured quantities are presented and calibrated, demonstrating that internal quantities of device operation can be uncovered. Several application areas are examined: spreading resistance and capacitance characterization of free carriers in III-V device structures; acquisition of electric potential and field distributions of semiconductor lasers, nanocrystals, and thin films; scanning voltage analysis on diode lasers—the direct observation of the internal manifestations of current blocking breakdown in a buried heterostructure laser, the effect of current spreading inside actively biased ridge waveguide lasers, anomalously high series resistance encountered in ridge lasers—as well as in CMOS transistors; and free-carrier measurement of working lasers with scanning differential spreading techniques. Applications to emerging fields of nanotechnology and nanoelectronics are suggested.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".