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Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication
Matter ( IF 17.3 ) Pub Date : 2020-11-02 , DOI: 10.1016/j.matt.2020.10.005
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.



中文翻译:

具有深度学习认证的防伪模式2D材料的多代折皱

物理不可克隆功能(PUF)是防伪的基石。但是,常规的基于PUF密钥的安全标签遇到了多个瓶颈,例如复杂的制造,专门且乏味的读取,较长的身份验证时间以及不足的稳定性。在这里,我们利用各种二维材料(2DM),包括Ti 3 C 2 T xMXene和氧化石墨烯以PUF模式构建多代微观结构。在顺序收缩之间引入了两种中间处理,即阳离子插层和水分诱导润滑,以无转移和可扩展的方式设计多尺度模式。开发了深度学习(DL)辅助软件,以对具有可分类特征的分层地形进行预分类。此后,在较小的数据库中进行搜索和比较,以缩短总体身份验证时间。2DM标签和DL辅助软件之间的协同作用实现了可靠且对环境稳定的防伪技术DeepKey,显示出出众的编码能力(> 10 144,494)和较短的身份验证时间(约3.5分钟)。我们的2DM防伪标签最终与QR码集成在一起,以提供两层信息安全性。

更新日期:2020-12-02
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