Tunable plasmonics on epsilon-near-zero materials: the case for a quantum carrier model
Bibliographic record
Abstract
The carrier density profile in metal-oxide-semiconductor (MOS) capacitors is computed under gating using two classical models - conventional drift-diffusion (CDD) and density-gradient (DG) - and a self-consistent Schrödinger-Poisson (SP) quantum model. Once calibrated the DG model approximates well the SP model while being computationally more efficient. The carrier profiles are used in optical mode computations to determine the gated optical response of surface plasmons supported by waveguides incorporating MOS structures. Indium tin oxide (ITO) is used as the semiconductor in the MOS structures, as the real part of its optical permittivity can be driven through zero to become negative under accumulation, enabling epsilon-near-zero (ENZ) effects. Under accumulation the predictions made by the CDD and SP models differ considerably, in that the former predicts one ENZ point but the latter predicts two. Consequently, the CDD model significantly underestimates perturbations in n e f f of surface plasmons (by ∼4×) and yields incorrect details in surface plasmon fields near ENZ points. The discrepancy is large enough to invalidate the CDD model in MOS structures on ENZ materials under accumulation, strongly motivating a quantum carrier model in this regime.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".