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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The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.
The codes used for plotting the data are available from the corresponding author upon reasonable request.
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The authors declare no competing interests.
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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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). https://doi.org/10.1038/s41928-021-00569-x