Broadband Measurement and Tuning of the Volatile Thermo‐Optic Properties in Chalcogenide Phase Change Materials
Bibliographic record
Abstract
Abstract Chalcogenide phase change materials (PCM's) are shown to exhibit a much‐overlooked giant switchable volatile thermo‐optic response across the visible/telecommunication bands. The first broadband measurement of this is presented in the alloys: Ge 2 Sb 2 Te 5 , Sb 2 Te 3 , Sb 2 S 3, and Sb 2 Se 3 , demonstrating that their response can exceed that of many commonly used volatile reconfigurable material platforms. Additionally, it is observed that the thermo‐optic response in some PCM alloys can be uniquely switched from positive to negative sign. To engineer this response based on application requirements, phase change metacoatings are introduced, relying on subwavelength interlayer nanostructuring of the PCM layer interleaved with transparent dielectric barriers. It is demonstrated that their thermo‐optic properties can be suppressed across broad frequency ranges and tuned by varying the periodicity within the metacoating, without altering the composition or stoichiometry of the PCM layer. This shows that neglecting thermo‐optical effects in photonic platforms with PCM inclusions during device design may lead to instabilities and noise due to intrinsic thermo‐optic drifts. It also highlights the potential for exploring new PCMs/PCM heterostructures to serve as the sole modulation layer in PIC architectures, meeting both volatile and nonvolatile signal modulation needs, promising significant reductions in device footprints/power consumption in classical and emerging quantum photonic architectures.
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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".