Strain Engineering Boosts Piezo-/Ferroelectricity in AlScN Alloy: Insights from First-Principles Calculations
Bibliographic record
Abstract
AlScN is a highly promising novel ferroelectric material featuring excellent high-temperature stability and CMOS compatibility, making it a potential candidate for 5G RF front-end filters, next-generation power devices, memories, and emerging in-memory computing devices. However, the rather mediocre piezoelectric coefficient and relatively large coercive field remain critical bottlenecks for its widespread adoption in applications. To provide theoretical guidance and effective strategies for optimizing the AlScN performance, we propose a synergistic regulation strategy based on alloying and strain engineering and conduct first-principles calculations using density functional theory to investigate the effects of Sc concentration and epitaxial tensile strain on the properties of AlScN. The proposed strategy is found to effectively enhance the piezoelectric strain coefficient ( d 33 > 300 pC·N –1 ) and electromechanical coupling coefficient ( k 33 2 ∼ 55%) of AlScN, and reduce its coercive field ( E C ), while maintaining a large polarization ( P sp > 68 μC·cm –2 ). The substantial increase in d 33 and k 33 2 is highly beneficial for optimizing the performance of bulk acoustic wave resonators for signal processing in RF applications. Meanwhile, the reduction in E C provides new opportunities for low-power ferroelectric memory devices, such as ferroelectric random-access memory and in-memory computing synaptic devices. The weakened bond strength and enhanced Born effective charge are found to be crucial in these performance optimizations. Furthermore, we examine the high-temperature stability of strain-engineered AlN-based piezo-/ferroelectric materials through ab initio molecular dynamics simulations. This work not only provides an effective strategy and valuable insights for physical property optimization in AlScN from the theoretical point of view but also clarifies the mechanisms of enhanced piezo-/ferroelectricity in wurtzite alloy systems by application of epitaxial strain and chemical modification.
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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".