Electromagnetic Physically-Unclonable Functions Generated by Graphene Radio-Frequency Circuits

无线技术的快速发展导致了无数的互联网连接设备，这些设备可以感知周围环境并共享传感器数据以及这些设备的身份，即所谓的物联网（iot）。然而，这些智能无线设备目前具有基于数字记忆的可负担得起的身份验证技术，容易受到各种网络攻击和设备克隆。假冒或恶意硬件带来的安全挑战引起了全世界的关注，因为它可能给社会造成重大经济损失，并对个人构成网络威胁。本研究旨在开发创新的电磁物理不可克隆功能（empuf），可作为无线通信和无线访问控制的超轻量，抗攻击认证模块。EMPUF的灵感来自使用人工智能来跟踪数十亿不同人类声音的声学特征的语音识别，它利用无线设备射频（RF）电路中的缺陷或样本特定噪声来生成加密密钥。在这种情况下，可以使用源自无线电波的任意波形和极化的特定于设备的唯一性来识别无线设备。在这个项目中，新型射频器件和电路将使用纳米材料，如具有高度随机和可重构电子特性的石墨烯，以最大限度地提高射频指纹的独特性。这个跨学科的研究结合了硬件安全、电磁学、射频电路、纳米电子学和人工智能，将为研究生、本科生和K-12学生提供多学科的研究经验。该项目将通过伊利诺伊大学的一系列项目，如工程项目中的女性、早期外展项目和早期研究学者项目，将研究、教育和社区外展活动结合起来，以增加女性和代表性不足的少数族裔在STEM领域的代表性。本研究旨在开发一类用于加密密钥生成和认证的新型强物理不可克隆功能（puf），作为抵御由大量资源稀缺的无线设备组成的网络中的网络攻击的第一道屏障。互补金属氧化物半导体（CMOS）技术的制造工艺变化已被广泛用于实现基于仲裁器、环形振荡器、触发器和静态随机存取存储器（SRAM）的puf和真随机数发生器。然而，将这一概念应用于低成本、小体积的物联网和智能设备仍然具有挑战性，这些设备的可用功率和存储容量有限。该项目提出了一种超轻量、节能的EMPUF，可以识别射频前端组件（如调制器、合成器、混频器和振荡器）中的信号变化，这些组件使用石墨烯为基础的器件。具体来说，石墨烯场效应晶体管（gfet）中由随机应变、缺陷和掺杂剂波动产生的高熵特性将被用来实现高性能的EMPUF实例。此外，gfet的可重构和可重置电子特性将被利用来产生冗余的大型挑战响应对（CRPs），从而实现强puf和超硅安全原语。一种计算效率高的机器学习算法也将被开发出来，从背景噪声中检索重要的射频足迹。这项研究的结果有望帮助建立高度通用的防伪解决方案，这在许多领域都迫切需要，例如无线访问控制，远程信息处理基础设施的安全，射频识别，加密无线通信和商品认证，等等。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Rapid advances in wireless technologies have led to a myriad of internet-connected devices, which sense the surroundings and share sensor data along with the identifications of these devices, the so-called Internet-of-Things (IoTs). However, these smart wireless devices with currently affordable authentication techniques based on digital memories are vulnerable to various cyber-attacks and device cloning. The security challenge posed by counterfeit or malicious hardware has attracted worldwide attention due to the potential significant economic loss to society and cyber threats to individuals. This research aims to develop innovative electromagnetic physical unclonable functions (EMPUFs) that can be used as an ultra-lightweight, attack-resilient authentication module for wireless communication and wireless access control. Inspired by speech recognition using artificial intelligence to track acoustic signatures of billions of different human voices, the EMPUF exploits imperfections or sample-specific noises in the radio-frequency (RF) circuitry of a wireless device to generate encryption keys. In this scenario, wireless devices can be identified using the device-specific uniqueness derived from the arbitrary waveform and polarization of the radio waves they produce. In this project, new types of RF devices and circuits will be built using nanomaterials, such as graphene with highly random and somewhat reconfigurable electronic properties, to maximize the uniqueness of RF fingerprints. This interdisciplinary research interfacing hardware security, electromagnetics, RF circuits, nanoelectronics, and artificial intelligence will provide graduate, undergraduate, and K-12 students with a multidisciplinary research experience. The project will combine research, education, and community outreach activities through a series of programs at the University of Illinois, such as Women in Engineering Program, Early Outreach Program, and Early Research Scholars Program, to increase the representation of women and underrepresented minorities in the STEM fields.This research aims to develop a new class of strong physical unclonable functions (PUFs) for cryptographic key generation and authentication, as the first barrier fending off cyber-attacks in a network consisting of abundant resource-scarce wireless devices. Manufacturing process variations of the complementary metal-oxide-semiconductor (CMOS) technology have been widely used to implement PUFs and true random number generators based on arbiters, ring oscillators, flip-flops, and static random-access memories (SRAM). However, it remains challenging to apply this concept to low-cost, small-footprint IoTs and smart devices that have limited available power and memory capacity. This project proposes an ultra-lightweight, energy-efficient EMPUF that identifies signal variations in the RF front-end components such as modulators, synthesizers, mixers, and oscillators built using graphene-based devices. Specifically, the high entropy inherent in graphene field-effect transistors (GFETs) originating from random strains, defects, and dopant fluctuations will be harnessed to realize high-performance EMPUF instances. Moreover, the reconfigurable and resettable electronic properties of GFETs will be exploited to generate a redundantly large challenge-response pairs (CRPs), enabling the practical realization of strong PUFs and beyond-silicon security primitives. A computationally efficient machine learning algorithm will also be developed to retrieve important RF footprints from background noises. The outcomes of this research are expected to help establish highly versatile anti-counterfeiting solutions, which are urgently needed in many fields, such as wireless access control, safety in telematics infrastructure, RF identification, encrypted wireless communications, and authentication of merchandise, to name a few.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.