Abstract

Amorphous materials are extensively utilized in electronic devices due to their exceptional optoelectronic properties. However, the disorder nature of their structure leads to a low thermal conductivity, which hampers effective thermal management of electronic devices. Understanding the correlation between microstructures and thermal transport in amorphous materials is crucial for modulating their thermal conductivity (TC). In this work, we performed molecular dynamics simulations in conjunction with an accurate machine learning potential to generate various amorphous Si (a-Si) structures through an atomic deposition procedure. The results demonstrate that increasing the substrate temperature during deposition procedure or conducting high-temperature secondary annealing significantly improves the short-range and mid-range ordering of a-Si. This improved structural ordering reduces atomic stresses and consequently boosts the TC of a-Si by more than 40 %. The spectral TC analysis reveals that elevated substrate temperature and annealing temperature primarily improve the TC of modes below 8 THz, corresponding to propagation and diffusion modes. Our work provides not only a strategy to tune the TC of amorphous materials via the deposition process, but also a deeper understanding of heat transport mechanisms in these materials.

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