IR Laser Modifications of Anodic Tantalum Pentoxide for Photonic Applications
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Bibliographic record
Abstract
Making waveguides requires modification of material along a waveguide path in such a way that conditions for light propagation are advantageous along the waveguide but not so much in surrounding material. Usually, waveguides are produced with micro or nano photolitographic techniques that later require some wet chemistry, and wet or dry etching processes, or in-diffusion processes. Here we are presenting our experiments on non photolitographic modification of electrochemically prepared tantalum pentoxide, Ta2O5, for possible photonic applications. The e-beam evaporated thin tantalum layers on glass were anodized at 23◦C in the 0.5% H2SO4 electrolyte. After anodization samples were rinsed with deionized water and blow dried with nitrogen gas. Laser material modifications were performed in an OPTOMEC, Inc. Aerosol Jet Printer, Model AJ300CE that was equipped not only with a pneumatic and an ultrasonic jet printing set ups, but also with a UV source and with a IR laser. The laser was static over a platen where a sample was located. The laser beam was perpendicular to the platen. Laser writing was done by moving the platen. The platen motion (+/- 1um) was computer controlled and that practically allowed any design. Any *.dxf design files were transformed into the program files that run the system. Straight lines were tested for various laser powers. The width of lines was laser power depended. The circular designs and random designs were generated at the constant laser power and at the constant platen speed. Zeiss optical microscope images of samples were collected without a filter. The microscope light was perpendicular to the surface of the samples. All laser inscribed patterns were very bright compared to the rest of the samples - Figure 1. Figure 1. The IR laser inscribed pattern in anodic Ta2O5 sample. The line with is ~115um. * Electrochemical Society Active Member Figure 1
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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 it