Improved phase change properties in layered Sc <sub>x</sub> In <sub>2−x</sub> Se <sub>3</sub> for multilevel information storage
Bibliographic record
Abstract
Abstract Phase change random access memory stores information by utilizing large resistance differences between crystalline and amorphous states of phase change materials (PCM). It would be highly advantageous to realize multilevel devices with single PCM system in nanoscale to improve memory capacity and reduce power consumption. Experimental results verify the possibility of multilevel phase change in two dimensional In 2 Se 3 thin layers with two stable single crystal phases (α and β). Here, we report, the phase change properties of layered In 2 Se 3 can be greatly improved by substitutional doping with Sc atoms based on first-principle calculations together with semiclassical Boltzmann transport theory. The lattice thermal conductivity κ L of both α- and β-Sc x In 2−x Se 3 are lower than that of the undoped models in the temperature range from 300 K to 700 K. Particularly, κ L of β-Sc 0.167 In 1.833 Se 3 at 300 K is 0.53 W m −1 K −1 , which is reduced by 60% compared to β-In 2 Se 3 . The out-of-plane electrical conductivities also show a significant decrease after Sc doping. These results indicate better performance of Sc-doped In 2 Se 3 PCM with minimized thermal crosstalk and lower RESET current. Moreover, we found the resistance difference between α and β phase can become 1–2 orders of magnitude larger in Sc 0.167 In 1.833 Se 3 , which is beneficial for the multilevel programming. The phase change process from α-In 2 Se 3 to its β phase with different Sc doping sites are calculated by ab-initio molecular dynamics simulations. It is found that with the introduction of Sc atoms in tetrahedral sites, phase change speed can be significantly increased. Our studies shed light on the modification of phase change properties of layered In 2 Se 3 with rare earth doping.
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 | 0.000 | 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".