Printable Epsilon‐Type Structure Transistor Arrays with Highly Reliable Physical Unclonable Functions

Author:

Wang Rui1,Liang Kun2,Wang Saisai3,Cao Yaxiong3,Xin Yuhan3,Peng Yaqian3,Ma Xiaohua1,Zhu Bowen2,Wang Hong1ORCID,Hao Yue1

Affiliation:

1. Key Laboratory of Wide Band Gap Semiconductor Technology School of Microelectronics Xidian University Xi'an 710071 China

2. Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Hangzhou 310024 China

3. Key Laboratory of Wide Band Gap Semiconductor Technology School of Advanced Materials and Nanotechnology Xidian University Xi'an 710071 China

Abstract

AbstractPrinted electronics promises to drive the future data‐intensive technologies, with its potential to fabricate novel devices over a large area with low cost on nontraditional substrates. In these emerging technologies, there exists a large digital information flow, which requires secure communication and authentication. Physical unclonable functions (PUFs) offer a promising built‐in hardware‐security system comparable to biometrical data, which can be constructed by device‐specific intrinsic variations in the additive manufacturing process of active devices. However, printed PUFs typically exploit the inherent variation in layer thickness and roughness of active devices. The current in devices with enough significant changes to increase the robustness to external environment noise is still a challenge. Here, printable epsilon‐type‐structure indium tin oxide transistor arrays are demonstrated to construct high‐reliability PUFs by modifying the coffee‐ring structure. The epsilon‐type structure improves the printing scalability, film quality, and device reliability. Furthermore, the print‐induced uncertainty along the channel thickness and length can lead to changes in the carrier concentration. Notably, the randomly distributed printing droplets in a small area significantly increase this uncertainty. As a result, the PUFs exhibit near‐ideal uniformity, uniqueness, randomness, and reliability. Additionally, the PUFs are resilient against machine‐learning‐based attacks with a prediction accuracy of only 55% without postprocessing.

Funder

National Natural Science Foundation of China

National Basic Research Program of China

Fundamental Research Funds for the Central Universities

Publisher

Wiley

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

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