Micro-nano technology creates the anti-counterfeiting “fingerprint” key of goods

Micro-nano processing is one of the two major foundations of nano research and has attracted widespread attention, however, with the emergence of various new devices and structures, conventional micro-nano processing methods can no longer fully meet the needs. This motivates people to explore unconventional machining methods that are more cost-effective and have higher processing capacity.

Liu Qian, a researcher at the National Nano Center, and his team have developed a variety of unconventional processing methods based on the newly developed new concept laser direct writing equipment. Recently, the team has made new progress in the research of physical non-replicable function (PUF) security labels, and the relevant results have been published online in Nature-Communications.

PUF production process and characterization. Photo courtesy of the National Nano Center

In the process of rapid social and economic development, counterfeit and shoddy goods are also becoming increasingly rampant, and traditional anti-counterfeiting labels face great challenges in their own security because of their deterministic construction mode. The uniqueness and unpredictability of PUF identification can be used as the “fingerprint” key of the product, fundamentally curbing the possibility of the label itself being forged. To this end, the researchers used the random fractal gold network structure generated by the principle of metal film dehumidification as PUF, and developed a new PUF anti-counterfeiting system composed of random fractal network identifier and deep learning recognition verification model, and demonstrated the multi-level anti-cloning ability of the PUF.

Establishment and performance display of deep learning recognition verification system. Photo courtesy of the National Nano Center

High-throughput patterned lithography (openwork stencils), thin film deposition, and one-step thermal annealing enable wafer-level PUF unit fabrication, reflecting the production characteristics of batch-based and low-cost (less than 1 cent per tag).

In order to apply it to practical anti-counterfeiting scenarios, the researchers developed an image PUF recognition verification system based on deep learning algorithm, which realized traceable, fast and high-precision (0% false positive) verification of 37,000 PUF identifiers with the help of ResNet50 classification neural network model, and proposed a dynamic database strategy to give the deep learning model extremely high database expansion capability, which theoretically broke the barrier of incompatibility between the establishment of a huge database and low time cost.

In addition, this PUF production is highly compatible with the microelectronics process flow, and is expected to be integrated with the component at the same time and complete the authenticity verification of the component unit. The developed PUF system can initially meet the needs of industrialization and is expected to promote the development and popularization of commercial PUF anti-counterfeiting technology. At present, the relevant technology has been authorized by the national invention patent. (Source: Zhang Shuanghu, China Science News)

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