Laser writing of 3D waveguides up to long-wave infrared: sensing and high refractive index contrast challenges
Bibliographic record
Abstract
Since the demonstration of Davis et al. in 1996, femtosecond laser direct inscription emerged as a powerful tool for the fabrication of three-dimensional photonic circuits. Even today, the performance of calculations based on the volume density of components would greatly benefit from the 3D capability of fs-laser inscription. Although several advanced 3D devices such as photonic quantum circuits and lab-on-a-chip were successfully fabricated, compactness is still limited by the minimum achievable waveguide bend radius. Another growing interest is the laser inscription in materials with transmission up to the mid- and long-wave infrared for applications such as micro-organism detection, environmental monitoring, medical diagnostic and optical communication in the second atmospheric window at 8–12 microns. In this spectral band, materials that can be drawn into fiber optics, such as fluoride and chalcogenide glasses, are expensive and fragile. On the other hand, laser inscription allows the fabrication of waveguides in virtually any material, even crystals, enabling new IR applications, especially for harsh environmental conditions. In this communication, we present our recent progress on these two topics. First, we demonstrate waveguide bend radii down to <400 µm, which is an important improvement over the minimum 10-mm radius reported previously. The high refractive index change allowing such tight bends is attributed to a femtosecond laser induced band gap shift (FLIBGS) in the material. We also report low loss depressed-cladding waveguide (DCW) in crystals for IR applications. We particularly demonstrate the challenging inscription of a large DCW for single-mode operation at 10.6 µm with propagation loss of <0.63 dB/cm. We also describe a technique using a cover slide with optical contact to inscribe waveguides at the bulk surface for refractometric sensing applications.
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".