Laser infrared photothermal radiometry of electronic solids: Principles and applications to industrial semiconductor Si wafers
Bibliographic record
Abstract
The physical principles and application case studies of the novel diagnostic technique of laser infrared photothermal radiometry (PTR) of semiconductors are presented. Following superband gap optical excitation, the signal consists of two contributions, one due to the de-exciting carrier density (plasma wave) and another from direct absorption and heating of the lattice (thermal wave). Multiparameter fits to frequency-domain amplitude and phase data have been developed to reliably measure recombination lifetime, τ surface recombination velocities (front and back surface), electronic, and thermal diffusivities. Applications case studies are presented, which demonstrate that lifetime measurements using PTR provide a most sensitive, convenient, and nonintrusive, remote industrial semiconductor metrology. The new metrology combines the features of several laboratory and commercial techniques currently available for industrial wafer (substrate and process) characterization (e.g., thermoreflectance, microwave reflectance, and surface photovoltage). The technology is capable of being used as a sensitive control of ion implantation, contamination monitor during oxidation and wafer cleans, and photoexcited carrier recombination lifetime measurements.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".