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  • Perspective
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Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies

Nature Computational Science volume 4, pages 322–333 (2024)Cite this article

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Abstract

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.

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Fig. 1: Schematics showing semiconductor material design and electronic device optimization.
Fig. 2: Summary of data-driven strategies for material design space exploration.
Fig. 3: ML-assisted computational framework for semiconductor functionalities and electronic devices.
Fig. 4: Two fundamental research paradigms in data-driven material growth experimental studies.

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Acknowledgements

L.Z. acknowledges funding support from the National Key Research and Development Program of China (grant number 2022YFA1402500) and the National Natural Science Foundation of China (grant numbers 62125402 and 62321166653).

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Author notes

  1. These authors contributed equally: Jiahao Xie, Yansong Zhou.

Authors and Affiliations

  1. 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

    Jiahao Xie, Muhammad Faizan, Zewei Li, Tianshu Li, Xinjiang Wang & Lijun Zhang

  2. State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China

    Yansong Zhou & Yuhao Fu

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  1. Jiahao Xie
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  4. Zewei Li
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  5. Tianshu Li
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Contributions

L.Z. led the preparation, writing and editing of this Perspective. J.X. contributed most of the text and figures. Y.Z. and Z.L. assisted in writing and figure preparation. X.W. and M.F. reviewed and refined the paper. All authors contributed to discussions and feedback.

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Correspondence to Xinjiang Wang or Lijun Zhang.

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Nature Computational Science thanks Senthilnath Jayavelu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Table 1

An overview of representative data-driven-method-empowered studies of semiconductor material design and device optimization research. This table summarizes the representative studies from this Perspective, highlighting their main discovery, data-driven method, public dataset and access link.

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Xie, J., Zhou, Y., Faizan, M. et al. Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies. Nat Comput Sci 4, 322–333 (2024). https://doi.org/10.1038/s43588-024-00632-5

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