Machine learning helps track semiconductor defects under more realistic temperature conditions
All materials contain inherent defects. These are not always harmful, since they can be used to engineer and introduce certain properties into a material. However, it is crucial to understand how they function and how they affect the material's properties.
Defects are inherent in all materials, but their impact varies depending on temperature. Traditionally, models and simulations assume zero Kelvin, the lowest possible temperature, to calculate defect properties. However, this approach may not accurately represent real-world conditions. Researchers at Chalmers University of Technology have developed a more effective framework that considers temperature effects, providing more realistic results.
Julia Wiktor, a professor leading the research group, explains that defects can introduce new energy levels within a semiconductor's band gap. The position of these defect levels is crucial for designing applications. The team modeled defects by studying missing atoms in crystal structures and observed how temperature affects their behavior.
Their findings, published in npj Computational Materials, demonstrate that temperature significantly influences defect behavior, and models based on zero Kelvin can miss these important changes. The study highlights the potential of machine learning to enhance materials modeling efficiency and accuracy, enabling researchers to better understand defect behavior in various materials and applications.
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