Al1−xScxSbyN1−y: An opportunity for ferroelectric semiconductor field effect transistor
Bibliographic record
Abstract
Abstract For the in-memory computation architecture, a ferroelectric semiconductor field-effect transistor (FeSFET) incorporates ferroelectric material into the FET channel to realize logic and memory in a single device. The emerging group III nitride material Al 1− x Sc x N provides an excellent platform to explore FeSFET, as this material has significant electric polarization, ferroelectric switching, and high carrier mobility. However, steps need to be taken to reduce the large band gap of ∼5 eV of Al 1− x Sc x N to improve its transport property for in-memory logic applications. By state-of-the-art first principles analysis, here we predict that alloying a relatively small amount (less than ∼5%) of Sb impurities into Al 1− x Sc x N very effectively reduces the band gap while maintaining excellent ferroelectricity. We show that the co-doped Sb and Sc act cooperatively to give a significant band bowing leading to a small band gap of ∼1.76 eV and a large polarization parameter ∼0.87 C/m 2 , in the quaternary Al 1− x Sc x Sb y N 1− y compounds. The Sb impurity states become more continuous as a result of interactions with Sc and can be used for impurity-mediated transport. Based on the Landau-Khalatnikov model, the Landau parameters and the corresponding ferroelectric hysteresis loops are obtained for the quaternary compounds. These findings indicate that Al 1− x Sc x Sb y N 1− y is an excellent candidate as the channel material of FeSFET.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".