Stochiometric Engineering of Reconfigurable Photo-Ionic Chalcogenides for Nanophotonic Applications
Bibliographic record
Abstract
Chalcogenide semiconductors, alloys of sulphur, selenium, and tellurium have been the active medium at the heart of many photonic technologies over the last few decades. They are being intensively explored for a range of emerging neuromorphic computing and compact telecom signal modulation applications. This is in part due to the wealth of compositionally tunable properties they exhibit at socio-economically important wavelength bands across the visible to infrared frequencies. This includes high optical nonlinearity, infrared transparency, and the ability to host various rare-earth and metallic ions [1]. Most notably, chalcogenides semiconductors have been widely utilized recently as a material platform for achieving reconfigurable metasurfaces and silicon photonic circuits, primarily through exploiting the thermally induced phase change mechanism inherent to these alloys. In such devices, the structure of the material reversibly changes between amorphous and crystalline phases, involving a power-hungry melt/quench process which can result in reduced device lifetime [2]. A severely overlooked property of metal-doped amorphous chalcogenide semiconductors (MdACs), particularly certain sulphides and selenides, is that they exhibit directional photo-induced long-range movement of their constituent metallic ions when exposed to light with a photon energy equivalent to or higher than the band gap of the glass. This “photoionic” movement can result in non-volatile changes of material properties (refractive index and conductivity) at the nanoscale facilitating robust, non-binary dynamic modulation of light without needing a phase transition. Recently, the photo-ionic modulation mechanism as an alternative, non-volatile reconfiguration phenomenon was demonstrated in amorphous silver-doped germanium selenide (GeSe) metasurfaces across visible frequencies [3]. In such devices, there is an interplay between the concentration of the silver ions in the chalcogenide host and the observed insertion loss and modulation contrast observed in the device. Therefore, to better understand the correct stoichiometry to utilize for increased static and dynamic signal/noise ratio and reduced device footprints, stoichiometric engineering techniques need to be used to map out the photo-ionic properties as a function of the ion to host concentrations for this emerging reconfigurable material platform.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".