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.

石墨烯具有一系列特性，使其适用于构建物联网设备。然而，此类设备的部署也可能需要开发合适的基于石墨烯的硬件安全原语。在这里，我们报告了一种物理不可克隆函数 (PUF)，该函数利用了石墨烯场效应晶体管的载流子传输中的紊乱。大量石墨烯场效应晶体管的狄拉克电压、狄拉克电导和载流子迁移率值遵循高斯随机分布，这使得器件可以用作 PUF。由此产生的 PUF 对基于预测回归模型和生成对抗神经网络的机器学习攻击具有弹性。由于石墨烯的忆阻特性，PUF 也可重新配置，无需任何物理干预和/或集成额外的硬件组件。此外，我们展示了 PUF 可以以超低功耗运行，并且具有可扩展性、随时间推移的稳定性以及对温度和电源电压变化的可靠性。