journal article·2022-08-12Open access

Unclonable Features via Electrospraying of Bulk Polymers

Abidin Esidir, N. Burak Kiremitler, Mustafa Kalay, Alper Basturk, M. Serdar Onses

ACS Applied Polymer Materials· Volume 4· Issue 8

Abstract

The ability to encode unclonable information using low-cost materials and processes is of significant interest for anti-counterfeiting and information security applications. In this study, we present a versatile approach based on electrospraying of polymer solutions to generate randomly positioned complex features as a form of physically unclonable function (PUF). The key advantage of this approach is that readily available low-cost bulk polymeric materials can form small and complex features using a simple process. Polymers of varying composition and molecular weight, together with different solvents and electrospraying conditions, are systematically explored to construct the parameter space for PUFs of varying characteristics. Besides the randomness in the spatial positions and sizes of features, the key advantage of the presented approach is the ability to generate complex 3D shapes, which are very difficult, if not impossible to fabricate with the most advanced fabrication techniques. The inclusion of photoluminescent molecules establishes an additional security layer. The additive nature of operation enables multiplexing, i.e., deposition of multiple materials on the same substrate. The fabricated PUFs have an average uniformity of 0.533 and uniqueness of 0.495, which are highly close to an ideal value of 0.5. The authentication is effectively performed using a feature detection algorithm without the need for markers and precisely defined rotation angles, greatly relaxing constraints associated with the imaging. Direct application of PUFs on the label of goods and authentication via a handheld microscope demonstrate the practical utility of the presented approach.
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Research context

OpenAlex topics: Physical Unclonable Functions (PUFs) and Hardware Security · Advanced Memory and Neural Computing · Electrohydrodynamics and Fluid Dynamics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

ERNAM - Nanotechnology Research and Application Center, Erciyes University, Kayseri 38039, Turkey

Department of Materials Science and Engineering, Erciyes University, Kayseri 38039, Turkey

Department of Electricity and Energy, Kayseri University, Kayseri 38039, Turkey

Department of Computer Engineering, Erciyes University, Kayseri 38039, Turkey

Artificial Intelligence and Big Data Application Research Center, Erciyes University, Kayseri 38039, Turkey

Funding

T?rkiye Bilimsel ve Teknolojik Arastirma Kurumu

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