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Graphene-based physically unclonable functions that are reconfigurable and resilient to machine learning attacks


Graphene has a range of properties that makes it suitable for building devices for the Internet of Things. However, the deployment of such devices will also likely require the development of suitable graphene-based hardware security primitives. Here we report a physically unclonable function (PUF) that exploits disorders in the carrier transport of graphene field-effect transistors. The Dirac voltage, Dirac conductance and carrier mobility values of a large population of graphene field-effect transistors follow Gaussian random distributions, which allow the devices to be used as a PUF. The resulting PUF is resilient to machine learning attacks based on predictive regression models and generative adversarial neural networks. The PUF is also reconfigurable without any physical intervention and/or integration of additional hardware components due to the memristive properties of graphene. Furthermore, we show that the PUF can operate with ultralow power and is scalable, stable over time and reliable against variations in temperature and supply voltage.

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Fig. 1: Fabrication, characterization and models for GFETs.
Fig. 2: Construction and properties of GFET PUFs.
Fig. 3: Reconfiguration of GFET PUFs.
Fig. 4: ML attack on GFET PUFs using predictive regression model.
Fig. 5: ML attack on GFET PUF using a GAN.
Fig. 6: Reliability and energy efficiency of GFET PUFs.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

Code availability

The codes used for plotting the data are available from the corresponding author upon reasonable request.


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Author information




S.D. conceived the idea, designed the experiments and wrote the paper. A.D., T.F.S. and D.B. performed the experiments. S.S.R. performed the machine learning attacks. P.S. developed the theoretical models. S.D. developed the empirical model. All the authors analysed the data, discussed the results, agreed on their implications and contributed to the preparation of the manuscript.

Corresponding author

Correspondence to Saptarshi Das.

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The authors declare no competing interests.

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Peer review information Nature Electronics thanks Derek Abbott and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–18, Discussions 1–24 and Tables 1 and 2.

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Dodda, A., Subbulakshmi Radhakrishnan, S., Schranghamer, T.F. et al. Graphene-based physically unclonable functions that are reconfigurable and resilient to machine learning attacks. Nat Electron 4, 364–374 (2021).

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