Simulation of a randomly percolated CNT network for an improved analog physical unclonable function

© 2024. The Author(s)..

Carbon nanotube networks (CNTs)-based devices are well suited for the physically unclonable function (PUF) due to the inherent randomness of the CNT network, but CNT networks can vary significantly during manufacturing due to various controllable process conditions, which have a significant impact on PUF performance. Therefore, optimization of process conditions is essential to have a PUF with excellent performance. However, because it is time-consuming and costly to fabricate directly under various conditions, we implement randomly formed CNT network using simulation and confirm the variable correlation of the CNT network optimized for PUF performance. At the same time, by implementing an analog PUF through simulation, we present a 2D patterned PUF that has excellent security and can compensate for error occurrence problems. To evaluate the performance of analog PUF, a new evaluation method different from the existing digital PUF is proposed, and the PUF performance is compared according to two process variables, CNT density and metallic CNT ratio, and the correlation with PUF performance is confirmed. This study can serve as a basis for research to produce optimized CNT PUF by applying simulation according to the needs of the process of forming a CNT network.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Scientific reports - 14(2024), 1 vom: 16. Apr., Seite 8811

Sprache:

Englisch

Beteiligte Personen:

Yang, Hyo-In [VerfasserIn]
Lee, Hanbin [VerfasserIn]
Ko, Jeonghee [VerfasserIn]
An, Yulim [VerfasserIn]
Min, Gyeongsu [VerfasserIn]
Kim, Dong Myong [VerfasserIn]
Kim, Dae Hwan [VerfasserIn]
Bae, Jong-Ho [VerfasserIn]
Lim, Meehyun [VerfasserIn]
Choi, Sung-Jin [VerfasserIn]

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Date Revised 19.04.2024

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1038/s41598-024-59584-5

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM371166322