Contactless non-destructive imaging of doping density and electrical resistivity of semiconductor Si wafers using lock-in carrierography
Bibliographic record
Abstract
Abstract A contactless non-destructive imaging method for spatially resolved dopant concentration, [2.2] N d , and electrical resistivity, ρ , of n - and p -type silicon wafers using lock-in carrierography images at various laser irradiation intensities is presented. Amplitude and phase information from wafer sites with known resistivity was employed to derive a calibration factor for accurate determination of the absolute carrier generation rate. A frequency-domain model based on the nonlinear nature of photocarrier radiometric signals was used to extract dopant density images. Lateral variations in the resistivity of an n -type and a p -type wafer obtained by means of this methodology were found to be in excellent agreement with those obtained with conventional 4-point probe measurements. This all-optical contactless method can be used as a non-destructive tool for doping density and electrical resistivity measurements and their images over large semiconductor areas. N d , ρ and their variances can be measured and mapped for the photovoltaic, micro- and opto-electronic industries including on wafers with oxides or surface treated layers for which contacting electrical measurements are impossible.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".