journal article·2023-11-01Open access

Pixelated Physical Unclonable Functions through Capillarity-Assisted Particle Assembly

Zazo Cazimir Meijs, Hee Seong Yun, Pascal Fandre, Geonhyeong Park, Dong Ki Yoon, Lucio Isa

Abstract

High Resolution Image Download MS PowerPoint Slide Recent years have shown the need for trustworthy, unclonable, and durable tokens as proof of authenticity for a large variety of products to combat the economic cost of counterfeits. An excellent solution is physical unclonable functions (PUFs), which are intrinsically random objects that cannot be recreated, even if illegitimate manufacturers have access to the same methods. We propose a robust and simple way to make pixelated PUFs through the deposition of a random mixture of fluorescent colloids in a predetermined lattice using capillarity-assisted particle assembly. As the encoding capacity scales exponentially with the number of deposited particles, we can easily achieve encoding capacities above 10 700 for sub millimeter scale samples, where the pixelated nature of the PUFs allows for easy and trustworthy readout. Our method allows for the PUFs to be transferred to, and embedded in, a range of transparent materials to protect them from environmental challenges, leading to improved stability and robustness and allowing their implementation for a large number of different applications.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Physical Unclonable Functions (PUFs) and Hardware Security · Nanofabrication and Lithography Techniques · 3D Printing in Biomedical Research

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

Affiliations

Laboratory for Soft Materials and Interfaces, Department of Materials, ETH Zurich, 8093 Zurich, Switzerland

Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung · National Research Foundation of Korea

References (49)

Show source-provided references
1. 10.1007/s10610-019-09414-610.1007/s10610-019-09414-6 ↗
2. 10.1038/s41467-019-14066-510.1038/s41467-019-14066-5 ↗
3. Archambeau, C.; Stryszowski, P.; Kazimierczak, M.; Pilichowski, E. Organisation for Economic Co-Operation and Development 2021 Report; Global Trade in Fakes, 2021.
4. 10.1038/s41928-020-0372-510.1038/s41928-020-0372-5 ↗
5. 10.1126/science.107437610.1126/science.1074376 ↗
6. 10.1109/JPROC.2014.232051610.1109/JPROC.2014.2320516 ↗
7. 10.1038/s41928-022-00788-w10.1038/s41928-022-00788-w ↗
8. 10.1126/sciadv.170138410.1126/sciadv.1701384 ↗
9. 10.1109/ACCESS.2015.250343210.1109/ACCESS.2015.2503432 ↗
10. 10.1126/sciadv.adf197810.1126/sciadv.adf1978 ↗
11. 10.1038/s41467-021-27278-510.1038/s41467-021-27278-5 ↗
12. 10.1038/s41570-017-003110.1038/s41570-017-0031 ↗
13. DeJean, G.; Kirovski, D. RF-DNA: Radio-Frequency Certificates of Authenticity. In Cryptographic Hardware and Embedded Systems - CHES 2007: Berlin, Heidelberg, 2007; pp 346–363.10.1007/978-3-540-74735-2_24 ↗
14. Chatterjee, B.; Das, D.; Sen, S. RF-PUF: IoT security enhancement through authentication of wireless nodes using in-situ machine learning. 2018 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2018; pp 205–208.10.1109/HST.2018.8383916 ↗
15. 10.1021/acsami.1c2080310.1021/acsami.1c20803 ↗
16. 10.1021/acsami.3c0219310.1021/acsami.3c02193 ↗
17. 10.1021/acsami.0c1110310.1021/acsami.0c11103 ↗
18. 10.1002/adma.20230213510.1002/adma.202302135 ↗
19. 10.1002/adma.20230307710.1002/adma.202303077 ↗
20. 10.1002/adma.20210254210.1002/adma.202102542 ↗
21. 10.1038/s41467-019-14070-910.1038/s41467-019-14070-9 ↗
22. 10.1021/acsanm.2c0162210.1021/acsanm.2c01622 ↗
23. 10.1016/j.cej.2022.13560110.1016/j.cej.2022.135601 ↗
24. 10.1021/acsami.2c1873710.1021/acsami.2c18737 ↗
25. 10.1002/adom.20210240210.1002/adom.202102402 ↗
26. 10.1109/tcad.2015.242495510.1109/tcad.2015.2424955 ↗
27. 10.1126/science.abj269110.1126/science.abj2691 ↗
28. 10.1039/D2MH00710J10.1039/D2MH00710J ↗
29. 10.1109/JSEN.2020.298607710.1109/JSEN.2020.2986077 ↗
30. 10.1021/acsami.0c1684610.1021/acsami.0c16846 ↗
31. 10.1002/adfm.20210867510.1002/adfm.202108675 ↗
32. 10.1039/D0MH00566E10.1039/D0MH00566E ↗
33. 10.1016/j.cej.2020.12835010.1016/j.cej.2020.128350 ↗
34. 10.1021/acsapm.2c0080310.1021/acsapm.2c00803 ↗
35. 10.1038/nnano.2007.26210.1038/nnano.2007.262 ↗
36. 10.1039/C7SM02496G10.1039/C7SM02496G ↗
37. 10.1021/ja011048v10.1021/ja011048v ↗
38. 10.1016/j.mee.2014.11.02410.1016/j.mee.2014.11.024 ↗
39. Cybels, T. Use Optical and Silicon based PUFs to Uniquely Identify Assets Based on Their Own Unique Physical Attributes, 2023. https://www.thalesgroup.com/en/cybels-protect-0 (accessed 03 01, 2023).
40. 10.1002/adfm.20210210810.1002/adfm.202102108 ↗
41. 10.1002/anie.20080525710.1002/anie.200805257 ↗
42. 10.1039/C4FD00250D10.1039/C4FD00250D ↗
43. 10.1021/ac102963x10.1021/ac102963x ↗
44. 10.1039/D1TC02883A10.1039/D1TC02883A ↗
45. 10.1145/584091.58409310.1145/584091.584093 ↗
46. Feiten, L.; Sauer, M.; Becker, B. On Metrics to Quantify the Inter-Device Uniqueness of PUFs. Cryptology ePrint Archive Paper 2016/320, 2016.
47. Maiti, A.; Casarona, J.; McHale, L.; Schaumont, P. A Large Scale Characterization of RO-PUF. In 2010 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), 2010; pp 94–99.10.1109/HST.2010.5513108 ↗
48. 10.1126/sciadv.150177910.1126/sciadv.1501779 ↗
49. 10.1088/2053-1591/aa864510.1088/2053-1591/aa8645 ↗