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Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication

Lin Jing, Qian Xie, Hongling Li, Kerui Li, Haitao Yang, Patricia Li Ping Ng, Shuo Li, Yang Li, Edwin Hang Tong Teo, Xiaonan Wang, Po-Yen Chen

Physical unclonable function (PUF) is a cornerstone of anticounterfeiting. However, conventional PUF key-based secure tags encounter several bottlenecks, such as complicated manufacturing, specialized and tedious readout, long authentication time, and insufficient stability. Here, we utilize various two-dimensional materials (2DMs), including Ti3C2Tx MXene and graphene oxide, to construct multigenerational microstructures as PUF patterns. Two intermediate treatments, cation intercalation and moisture-induced lubrication, are introduced in between sequential contractions to engineer the multiscale patterns in a transfer-free and scalable fashion. A deep learning (DL)-facilitated software is developed to pre-categorize the hierarchical topographies with classifiable features. Thereafter, the search-and-compare is conducted within a smaller database to shorten the overall authentication time. The synergy between 2DM tags and DL-facilitated software enables a reliable and environmentally stable anticounterfeiting technology, DeepKey, showing superior encoding capacity (>10144,494) and short authentication time (∼3.5 min). Our 2DM anticounterfeiting tag is finally integrated with QR codes to provide two-layer information security.

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