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

无线技术的快速发展已经产生了无数的互联网连接设备，这些设备感测周围环境并沿着这些设备的标识共享传感器数据，即所谓的物联网（IoT）。然而，这些智能无线设备与目前负担得起的认证技术的基础上，数字存储器是脆弱的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已经引起了全世界的关注，因为它可能给社会带来重大的经济损失，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），可用作无线通信和无线访问控制的超轻量，攻击弹性认证模块。受使用人工智能跟踪数十亿不同人类声音的声学特征的语音识别的启发，EMPUF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，无线设备可以使用从它们产生的无线电波的任意波形和极化导出的设备特定的唯一性来识别。在该项目中，将使用纳米材料构建新型RF器件和电路，例如具有高度随机性和可重新配置电子特性的石墨烯，以最大限度地提高RF指纹的独特性。这种跨学科的研究接口硬件安全，电磁学，射频电路，纳米电子学和人工智能将提供研究生，本科生和K-12学生多学科的研究经验。该项目将通过伊利诺斯大学的一系列项目，如妇女参与工程项目、早期推广项目和早期研究学者项目，将联合收割机研究、教育和社区推广活动结合起来，增加妇女和代表性不足的少数民族在STEM领域的代表性。这项研究旨在开发一种新的强大的物理不可克隆功能（PUF）用于加密密钥生成和认证，作为在由大量资源稀缺的无线设备组成的网络中抵御网络攻击的第一屏障。互补金属氧化物半导体（CMOS）技术的制造工艺变型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和真随机数生成器。然而，将这一概念应用于低成本、占地面积小的物联网和可用功率和内存容量有限的智能设备仍然具有挑战性。该项目提出了一种超轻量、节能的EMPUF，可识别RF前端组件中的信号变化，例如使用石墨烯器件构建的调制器、合成器、混频器和振荡器。具体而言，石墨烯场效应晶体管（GFET）中源自随机应变、缺陷和掺杂剂波动的固有高熵将被利用来实现高性能EMPUF实例。此外，将利用GFET的可重新配置和可重新设置的电子特性来生成冗余的大型挑战-响应对（CRP），从而实现强PUF和超硅安全原语的实际实现。还将开发一种计算效率高的机器学习算法，以从背景噪声中检索重要的RF足迹。这项研究的成果预计将有助于建立高度通用的防伪解决方案，这是迫切需要在许多领域，如无线访问控制，安全的远程信息处理基础设施，射频识别，加密无线通信，和商品认证，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.