Globalization of semiconductor industry has led to unprecedented challenges in design, verification, test, security and trustworthiness of contemporary electronics. With parts sourced from various suppliers across the globe, who are beyond the auspices of the original designers and system integrators, serious concerns arise regarding integrity and trustworthiness of electronic circuits, which can be compromised by malicious actors at any stage of the component manufacturing and system assembly process. Among the variety of such concerns, recycled electronics sold as new by malicious suppliers/vendors pose a key threat to mission-critical end applications such as military systems, financial institutions, transportation security, and poser distribution infrastructure. Recycled electronics may experience performance degradation due to aging and, more importantly, may have shorter-than-expected lifetime, thereby raising serious reliability and safety issues. To combat this problem, statistical methods have been proposed in the past decade as a time-efficient and cost-effective approach to identify recycled electronics sold as new. Initially demonstrating in the context of detecting recycled Integrated Circuits (ICs), such methods are based on using statistics and machine learning to outline the parametric signatures of known brand-new ICs and, thereby, weed out ICs that do not fit the expected profile. This article summarizes the current state of knowledge in recycled IC detection schemes and discusses how these methods have inspired, over time, the development of similar solutions in a plethora of domains, including identification of recycled Field Programmable Gate Arrays (FPGAs), recycled hardware components in a network, and recycled Static Random-Access Memories (SRAMs)/Flash memories
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