Photoionic Driven Movement of Metallic Ions as a Nonvolatile Reconfiguration Mechanism in Amorphous Chalcogenide Metasurfaces
Bibliographic record
Abstract
Abstract Chalcogenide glasses have been widely adopted as a material platform for achieving reconfigurable metamaterials and metasurfaces, primarily through exploiting nonvolatile phase transitions inherent to these semiconductors. In such devices, the atomic lattice of the nanostructured medium reversibly changes between amorphous and crystalline phases, invoked through an energy‐intensive melt/quench process that can reduce device endurance due to chemical and geometrical drift. Metal‐doped amorphous chalcogenide semiconductors (MdACs) exhibit a directional photoinduced movement of their constituent metal‐ions when exposed to light with a photon energy equivalent or higher than the bandgap of the host chalcogenide glass. This “photoionic” movement results in nonvolatile changes of refractive index and conductivity at the nanoscale enabling a nonvolatile, nonbinary dynamic modulation of light removing the need for a phase transition. It is shown here that this photoionic movement in silver‐doped amorphous germanium selenide metasurfaces enables reversible optical switching. Understanding and integrating the photoionic mechanism within various optoelectronic device platforms presents significant potential for realizing a range of nonvolatile optically reconfigurable nanophotonic devices for emerging display, data storage, and signal processing applications.
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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".