Band structures of laterally coupled quantum dots, accounting for electromechanical effects
Bibliographic record
Abstract
In a series of recent papers we demonstrated that coupled electro-mechanical effects can lead to pronounced contributions in band structure calculations of low dimensional semiconductor nanostructures (LDSNs) such as quantum dots, wires, and even wells. Some such effects are essentially nonlinear. Both strain and piezoelectric effects have been used as tuning parameters for the optical response of LDSNs in photonics, band gap engineering and other applications. However, these effects have been largely neglected in literature while laterally coupled quantum dots (QDs) have been studied. The superposition of electron wave functions in these QDs become important in the design of optoelectronic devices as well in tayloring properties of QDs in other applications areas. At the same time, laterally grown QDs coupled with electric and mechanical fields are becoming increasingly important in many applications of LDSN-based systems, in particular where the tunneling of electron wave function through wetting layer (WL) becomes important and the distance between the dots is treated as a tuning parameter. Indeed, as electric and elastic effects are often significant in LDSNs, it is reasonable to expect that the separation between the QDs may also be used as a tuning parameter in the application of logic devices, for example, OR gates, AND gates and others. In this contribution, by using the fully coupled model of electroelasticity, we build on our previous results while analyzing the influence of these effects on optoelectronic properties of QDs. Results are reported for III-V type semiconductors with a major focus given to GaN/AlN based QD systems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".