Near-IR reconfigurable 1D Ag grating Fabry–Perot absorber hybridized with phase-change material GSST
Bibliographic record
Abstract
(GSST) has recently received a lot of attention due to the large index contrast between its amorphous and crystalline states with significantly low optical loss in the optical to near-IR spectrum. In this paper, we propose a tunable and reconfigurable hybrid PCM plasmonic nanostructure composed of a spacer layer of GSST sandwiched between a Ag back reflector and a 1D Ag Fabry-Perot grating structure. We use the finite element method (FEM) to numerically calculate the light absorption, absorption contrast, and figure of merit of the plasmonic nanostructure for both the amorphous and crystalline state of the GSST. Our calculations show that with constant structural variation the observed multimode absorption is drastically modified when the GSST undergoes a phase change from the amorphous to the crystalline state. The absorption contrast spectrum, which is defined as the absorption difference between the amorphous and crystalline state of GSST, shows four extrema modes between 70% and 89%. The figure of merit spectrum shows two large values of 44.39 and 37.78 at the 1502 nm and 2063 nm wavelengths, respectively. We also address the observed modes in the absorption contrast spectrum through spatial representation of the enhanced electric field distribution at their corresponding wavelengths. We show how the phase change in the GSST spacer can control the coupling between the optical cavity modes and the Ag surface plasmon resonance modes in the cavities and GSST spacer strip boundaries. The findings in this paper may open new avenues toward the design of next-generation photonic systems such as thermal emission controllers, sensors, ranging holograms, modulators and optical detection devices.
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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) | 0.000 | 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 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".