Hardware Security For and Beyond CMOS Technology: An Overview on Fundamentals, Applications, and Challenges <s> CNTs and CNTFETs. <s> Hardware-oriented security and trust has traditionally relied on the dominant CMOS technology to develop security primitives and provide protection against different attacks and vulnerabilities. With CMOS nearly reaching its fundamental scaling limit and the shortcomings of current solutions, researchers are now looking to exploit emerging nanoelectronic devices for various security applications. In this paper, we discuss the unique features of three emerging nanoelectronic technologies, namely, phase-change memory, grapheme, and carbon nanotubes, and analyze how these features can aid in hardware security and trust. In addition, we present challenges and future research directions about how to effectively integrate emerging nanoscale devices into hardware security. We emphasize that an interdisciplinary initiative is needed for emerging technologies to reach their full potential in security and trust applications. <s> BIB001 </s> Hardware Security For and Beyond CMOS Technology: An Overview on Fundamentals, Applications, and Challenges <s> CNTs and CNTFETs. <s> Physical unclonable function (PUF) is an advanced hardware security technology. Most conventional encryption approaches rely on the secure keys stored in nonvolatile memory, which are vulnerable to physical attacks. In contrast, PUF exploits the hardware fabrication variations to generate secure keys. As there are significant fabrication induced variations in carbon nanotube (CNT)-based circuits, they are natural candidates for building highly secure PUFs. However, existing PUFs are reported to be vulnerable to machine learning modeling attacks. In this paper, we develop a novel CNT PUF design through leveraging Lorenz chaotic system. Lorenz chaotic system magnifies the differences among responses of similar challenges. This salient feature gives the superior security performance, making the PUF design resistant to machine learning modeling attacks. Our experimental results demonstrate that these attacking methods can achieve very high bit-wise prediction rates for the standard CNT PUF. In contrast, when hacking the proposed Lorenz chaotic system-based CNT PUF, they become much less effective, where only up to 55% bit-wise prediction rates can be achieved. <s> BIB002
In BIB002 , the authors propose PUFs which leverage the manufacturing variability of CNTs along with the notion of Lorenz chaotic systems. The latter serves to enhance the decorrelation of inputs and outputs for such PUFs and, thus, renders them more resilient against machine-learning attacks. In  , the authors conduct a simulations-based study on CNT-FETS concerning Trojan detection, power side-channel leakage, and camouflaging, and they find that CNTFETs are more promising in all aspects when compared to the traditional CMOS technology. In BIB001 , the authors review the use of CNTS for PUFs, TRNGs, and also propose the technology to be used for novel sensors detecting microprobing or other invasive attacks.