Structural destabilization and optoelectronic degradation of crystalline Ge₂Sb₂Te₅ phase-change memory in low-earth orbit: A multiscale simulation study
Bibliographic record
Abstract
The development of radiation-tolerant phase-change memories is essential for next-generation space technologies, yet their structural and optoelectronic responses to electron irradiation in low-Earth-orbit (LEO) environments remain insufficiently understood. In this work, we integrate ab initio molecular dynamics (AIMD) with Monte Carlo (MC) simulations to uncover the atomistic damage mechanisms governing crystalline Ge₂Sb₂Te₅ (GST). Our directional and sublayer-resolved analyses show that radiation susceptibility and resilience are strongly governed by variations in Milliken bond populations (MBP) and the corresponding local bonding strengths within the GST lattice. Notably, van der Waals (vdW) planes with higher MBP act as defect sinks, buffering displacement cascades at threshold levels. AIMD simulations reveal lower energy damage thresholds for Te primary knock-on atoms (PKAs) than for Sb and Ge, leading to earlier onset of order–disorder transitions and metallic collapse. Void formation at these damage thresholds promotes the accumulation of wrong bonds and homopolar configurations, whereas non-void cascades preferentially maintain ABAB stacking motifs and exhibit higher structural resilience. This contrast is consistently supported by short- and medium-range order metrics, as well as by our newly introduced Milliken Ring Population (MRP) analysis. At the electronic damage dose (∼3 × 10⁵ MeV/g), GST exhibits rapid band-gap collapse, a sharp rise in the imaginary dielectric function, and a pronounced decline in the real dielectric function, most severe for Te PKAs. These findings establish a clear mechanistic foundation for irradiation-induced disorder in GST and offer methodological insights that can guide the design of radiation-resilient phase-change memory technologies for aerospace applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".