Xie Jiahao, Zhou Yansong, Faizan Muhammad, Li Zewei, Li Tianshu, Fu Yuhao, Wang Xinjiang, Zhang Lijun
State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China.
Nat Comput Sci. 2024 May;4(5):322-333. doi: 10.1038/s43588-024-00632-5. Epub 2024 May 23.
In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.
在摩尔定律后的时代，电子学的进步依赖于发现更优质的半导体材料以及优化器件制造。借助新兴的数据驱动策略增强的计算方法，为传统的试错法提供了一种有前景的替代方案。在这篇观点文章中，我们通过阐述数据驱动的计算框架在探索材料设计空间、预测半导体特性以及优化器件制造方面的进展，突出了这些框架对增强半导体发现和器件开发的作用，并对这些领域中的挑战与机遇进行了总结讨论。
