Plasmonic Anticounterfeit Tags with High Encoding Capacity Rapidly Authenticated with Deep Machine Learning

Counterfeit goods create significant economic losses and product failures in many industries. Here, we report a covert anticounterfeit platform where plasmonic nanoparticles (NPs) create physically unclonable functions (PUFs) with high encoding capacity. By allowing anisotropic Au NPs of different sizes to deposit randomly, a diversity of surfaces can be facilely tagged with NP deposits that serve as PUFs and are analyzed using optical microscopy. High encoding capacity is engineered into the tags by the sizes of the Au NPs, which provide a range of color responses, while their anisotropy provides sensitivity to light polarization. An estimated encoding capacity of 270(n) is achieved, which is one of the highest reported to date. Authentication of the tags with deep machine learning allows for high accuracy and rapid matching of a tag to a specific product. Moreover, the tags contain descriptive metadata that is leveraged to match a tag to a specific lot number (i.e., a collection of tags created in the same manner from the same formulation of anisotropic Au NPs). Overall, integration of designer plasmonic NPs with deep machine learning methods can create a rapidly authenticated anticounterfeit platform with high encoding capacity.

Automated summary

The study presents a covert anticounterfeit platform utilizing plasmonic nanoparticles to create physically unclonable functions with high encoding capacity. The platform allows for facile tagging of diverse surfaces with NP deposits through the use of anisotropic Au NPs of different sizes, providing sensitivity to light polarization and a range of color responses. Integration of designer plasmonic NPs with deep machine learning methods enables rapid authentication and high encoding capacity, with accurate matching to specific products leveraging descriptive metadata.