Exploring structural and mechanical properties of SiNx thin films on SiO2 substrate via molecular dynamics simulations
Bibliographic record
Abstract
• Investigated intrinsic stresses in SiN films using advanced molecular dynamics simulations to address integration challenges. • Demonstrated intrinsic stress control from 1 GPa tensile to −0.6 GPa compressive by varying deposition energy. • Predicted critical thicknesses for SiN film cracking, ranging from 200 nm to 4 µm, under varying deposition conditions. • Revealed the impact of Si-to-N-rich compositions on stress, Young’s modulus, and toughness of SiN films. • Explored substrate temperature and atom bombardment energy effects on SiN film properties, aiding deposition understanding. Silicon nitride (SiN x ) and silicon dioxide (SiO 2 ) are crucial in microelectronic and photonic devices, where their interface affects performance and reliability. This study explores the effects of growth conditions on the mechanical properties of amorphous SiN x films on SiO 2 substrates using molecular dynamics simulations. We address key integration challenges, focusing on mechanical residual stresses and surface defects. We assess the impact of substrate temperature, atom bombardment energy, and SiN x composition on intrinsic stress, Young’s modulus, and film toughness, focusing on the film’s susceptibility to failure and cracking. Our analysis includes deposition simulations at room temperature and 1100 K, with kinetic energies from 0.01 eV to 1 eV per atom. We investigate SiN x compositions from Si-rich to N-rich compositions to evaluate their effect on mechanical properties. The Tersoff potential for multi-body interactions facilitates a comprehensive examination of factors influencing mechanical stress and cracking. We numerically reproduced SiN x films with intrinsic stress values from 1 GPa tensile to −0.6 GPa compressive by varying deposition energy. For tensile films, we calculated critical thicknesses at which cracking occurs, from 200 nm to 4 µm. These insights refine deposition techniques, enhance mechanical durability, and support the development of reliable SiN optical platforms for photonic integrated circuits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".