A novel physical unclonable function (PUF) using 16 × 16 pure-HfOx ferroelectric tunnel junction array for security applications. (7th September 2021)

• Record Type: Journal Article
• Authors: Yu, Junsu; Min, Kyung Kyu; Kim, Yeonwoo; Kim, Sihyun; Hwang, Sungmin; Kim, Tae-Hyeon; Kim, Changha; Kim, Hyungjin; Lee, Jong-Ho; Kwon, Daewoong; Park, Byung-Gook
• Is Part Of: Nanotechnology. Volume 32:Number 48(2021)
• Publication Date: 2021-09-07
• Subjects: ferroelectric tunnel junctions -- physical unclonable functions -- FTJ crossbar arrays; Nanotechnology -- Periodicals; Nanotechnology -- Periodicals; Nanotechnology; Publications périodiques; Nanotechnologies; Periodicals; 620.5
• DOI: 10.1088/1361-6528/ac1dd5
• Languages: English
• ISSNs: 0957-4484

Abstract

Abstract: As the computing paradigm has shifted toward edge computing, improving the security of edge devices is attracting significant attention. However, because edge devices have limited resources in terms of power and area, it is difficult to apply a conventional cryptography system to protect them. On the other hand, as a simple security application, a physical unclonable function (PUF) can be implemented without power and area problems because it provides a security key by utilizing process variations without additional external circuits. Ferroelectric tunnel junctions (FTJs) are 2-terminal devices that store information by changing the resistance of a ferroelectric material, where the resistance is determined by the polarization states of the ferroelectric domains. Because polycrystalline ferroelectric materials have a multi-domain nature, domain variation can also be used as a randomness source to induce cell-to-cell variations along with process variations. In this paper, we demonstrate PUF operations of a low-power, small area 16 × 16 hafnium oxide (pure-HfO x )-based FTJ array using certain metrics. It is clear that the proposed array consisting of scaled FTJs has adequate randomness for security applications such that the array-level PUF operations are robust against model-based machine learning attacks.