Investigating Strain in Silicon‐on‐Insulator Nanostructures by Coherent X‐ray Diffraction
Bibliographic record
Abstract
Coherent X-ray Diffraction Imaging (CDI) has emerged in the last decade as a promising high-resolution lens-less imaging approach for the characterizations of various samples. This chapter introduces the principles of forward scattering CDI and Bragg geometry CDI(BCDI). The chapter presents the latest progress on the application of BCDI in investigating the strain relaxation behavior in nanoscale patterned strained-silicon-on-insulator (sSOI) materials, aiming to understand and thereby engineer strain for the design and implementation of new generation semiconductor devices. In a coherent diffraction imaging (CDI) measurement, as the detector can only collect the square modulus of the complex wavefields diffracted from the sample, any associated phase information is lost. To reconstruct the image of the sample from its diffraction pattern (either Fresnel or Fraunhofer regime), it is necessary to retrieve the phase information from the measured intensity.
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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 | 0.000 |
| 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".