Laser machining of silicon for photonics components
Bibliographic record
Abstract
Machining silicon has been a mainstay of the semiconductor industry for decades. While this has traditionally being done through wet chemical etching, other techniques are worth exploring. This is especially true for efforts in the pursuit of integrating photonics devices on a single chip to facilitate their production, for which a non-wet, non-contact, arbitrary-shape milling of silicon would be best. In this paper, we present our latest work in the laser micromachining of silicon. A kilohertz-repetition-rate diode-pumped Nd:YLF laser (in infrared, green or ultraviolet modes) is focused on the surface of silicon wafers in a chlorine atmosphere for enhanced magnitude and control of the etching rate. In the chlorine atmosphere, much less debris is deposited on the surface around the cut, sub-damage threshold machining is achieved for a better control of the etching depth, and etching rates ranging from 20-300,000 µm3/s have been measured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".