Researchers develop computational framework for semiconductor defect analysis
Chalmers University of Technology has introduced a new model to analyze how temperature affects atomic-level defects in semiconductor materials.
Standard semiconductor modeling has historically relied on calculations at zero Kelvin, which fails to capture how materials behave in real-world environments. Researchers at Chalmers University of Technology have developed a new computational framework that integrates temperature effects into these simulations.
Led by Professor Julia Wiktor, the team utilized machine learning to overcome the high computational costs previously associated with such complex models. The framework allows for the tracking of point defects—such as missing or misplaced atoms—across a full temperature range. By observing how energy levels within a semiconductor's band gap shift due to temperature, researchers can more accurately predict how materials will perform in functional devices.
Published in npj Computational Materials, the study highlights the importance of accounting for temperature to avoid potential design errors in material engineering. This approach provides a scalable tool for researchers working on emerging technologies, including solar cell applications, where material stability is critical.