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Two-dimensional materials for next-generation computing technologies

Abstract

Rapid digital technology advancement has resulted in a tremendous increase in computing tasks imposing stringent energy efficiency and area efficiency requirements on next-generation computing. To meet the growing data-driven demand, in-memory computing and transistor-based computing have emerged as potent technologies for the implementation of matrix and logic computing. However, to fulfil the future computing requirements new materials are urgently needed to complement the existing Si complementary metal–oxide–semiconductor technology and new technologies must be developed to enable further diversification of electronics and their applications. The abundance and rich variety of electronic properties of two-dimensional materials have endowed them with the potential to enhance computing energy efficiency while enabling continued device downscaling to a feature size below 5 nm. In this Review, from the perspective of matrix and logic computing, we discuss the opportunities, progress and challenges of integrating two-dimensional materials with in-memory computing and transistor-based computing technologies.

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Fig. 1: Schematic illustration of matrix and logic computing.
Fig. 2: Unique characteristics of 2D materials and opportunities for next-generation computing.
Fig. 3: Dimensional scaling advantages of 2D-material-based transistors.
Fig. 4: Advantages of 2D materials for integration.
Fig. 5: Roadmap of integrating 2D materials for next-generation computing.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61925402, 61851402 and 61734003), Science and Technology Commission of Shanghai Municipality (19JC1416600), National Key Research and Development Program (2017YFB0405600), Shanghai Education Development Foundation and Shanghai Municipal Education Commission Shuguang Program (18SG01).

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Correspondence to Peng Zhou.

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Liu, C., Chen, H., Wang, S. et al. Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15, 545–557 (2020). https://doi.org/10.1038/s41565-020-0724-3

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