Effect of large deflection angle on the laser intensity profile produced by AOD scanners in high precision manufacturing
Bibliographic record
Abstract
Laser beam scanners have found wide applications in a variety of laser-assisted advanced microprocessing technologies, such as printing, patterning and doping. Traditional galvo-scanners affect the accuracy of beam positioning and repeatability in high precision manufacturing due to mechanical motion of the mirrors and backlash errors. An Acousto-optic Deflector (AOD), which is made of a transparent photoelastic medium bonded to a piezoelectric transducer, is a promising device to overcome these limitations. AODs are commonly used in laser direct writing systems to provide flexible and high-speed beam scanning with high precision and accuracy. We have developed an analytic model based on Bessel functions, which will be referred to as Bessel model, to calculate the strain tensor, stress tensor and stress-induced birefringence, and the change in the refraction index of the crystal. This refraction index variation produces the volume phase grating and provides a mechanism for diffraction, and consequently, deflection of the laser beam as it propagates through the AOD crystal. Various laser parameters, such as the diffraction efficiency and the laser intensity of the diffraction pattern at different deflection angles are studied in this paper.
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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 | 0.002 |
| 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".