Inline coherent imaging of laser micromachining
Bibliographic record
Abstract
In applications ranging from noncontact microsurgery to semiconductor blind hole drilling, precise depth control of laser processing is a major challenge. Even expensive a priori characterization cannot compensate for material heterogeneity and stochasticity inherent to the ablation process. Here we use in situ depth imaging to guide the machining process in real time. W e image along the machining beam axis at high speeds (up to 300 kHz) to provide real-time feedback, even in high aspect ratio holes. The in situ metrology is based on coherent imaging (similar to optical coherence tomography) and is practical for a wide-range of light sources and machining processes (e.g., thermal cutting or ultrafast nonlinear ablation). Coherent imaging has a high dynamic range (>; 60 dB) and strongly rejects incoherent signals allowing weak features to be observed in the presence of intense machining light and plasmas. High axial resolution (~10 μm) requires broadband imaging light but the center wavelength can be chosen appropriate to the application. Infrared light (wavelength: 1320 ± 35 nm) allows simultaneous monitoring of both surface and subsurface interfaces in non-absorbing materials like tissue and semiconductors. By contrast, silicon based detector technology can be used with near infrared imaging light (805 ± 25 nm) enabling high speed acquisition and low cost implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".