Ultrashort pulse laser machining of microchannels in polymers and glass materials for fabrication of a microfluidic optical switch
Bibliographic record
Abstract
Polymers and glasses are important materials for non-silicon MEMS devices. Precision structuring of such materials has been plagued by the lack of proper tools capable of producing the required intricate details and finish quality. Machining with high peak power, short pulse lasers has become a potential technique for such applications due to the reduced thermal damage, high precision, small feature size and flexibility in pattern generation. In particular, the nanosecond and femtosecond pulsed lasers offer significant advantages with the ability to deposit energy in materials in a very short time interval, hence ensuring efficient conversion of the energy for material removal. In this paper, the results of using femtosecond laser to process polycarbonate, aluminosilicate glasses and nanosecond laser processing of aluminosilicate glasses are discussed. High quality microchannels in polycarbonate and glass substrates for a bubble switch have been created. The critical dimensions are at the micro scale. No cracks or burrs were observed by a scanning electron microscope (SEM). The major processing variables such as cutting speed, energy fluence and power stability were investigated for their effects on machining quality. Microchannels with sub-micron surface roughness and high-aspect ratios were demonstrated. Preliminary testing of a bubble switch, which is based on the thermo-capillary effect, confirmed the working principle of the device; but indicated that the channels would have to be more narrower to achieve greater switching speeds.
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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".