**1. Introduction**

The rapid growth of sensors and the Internet of Things (IoT) has the potential to transform society, the economy, and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The sensitive information can be leaked from side channels, including power consumption and electromagnetic (EM) emissions. Some devices are simply controlled by a simple wake-up signal to activate data transmission without two-way authentication. Moreover, the wireless charging techniques that allow energy constrained devices and electric cars to stay connected and operate continuously provide another entry point to exploit the sensitive information and the vulnerability in the power domain, as shown in Figure 1a. The vulnerability of those wireless devices to hacking or exploitation has emerged as a major concern on both security and public safety. For instance, because the electronic devices may continue receiving and transmitting signals while they are being wirelessly powered, the data activities are exposed to the energy source. Nevertheless, state-of-the-art cybersecurity approaches are mainly focused on software and digital modules. Security measures are not integrated in the analog/radio frequency (RF) domain to verify signal and power sources or to suppress the side-channel emissions in real time. To bridge the gap, this study presents a self-testing approach incorporating nanoscale EM sensing devices and learning algorithms to detect threats directly at the RF and analog front-end. As shown in Figure 1b, the EM sensors are integrated into the RF/analog front-end through post processing to monitor the EM emissions from power wires and critical signal nodes. Machine-learning modules were developed to analyze the sensed data for threat and vulnerability detection. Combing emerging material, device, circuit, and system concepts, this study developed a built-in threat detection approach in the RF/analog domain without degrading the performance while achieving good energy efficiency.

**Citation:** Chen, E.; Kan, J.; Yang, B.-Y.; Zhu, J.; Chen, V. Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection. *Sensors* **2021**, *21*, 8288. https:// doi.org/10.3390/s21248288

Academic Editors: Zihuai Lin and Wei Xiang

Received: 31 October 2021 Accepted: 8 December 2021 Published: 11 December 2021

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**Figure 1.** (**a**) Security vulnerability of electromagnetic emissions and wireless charging (figure credit for wireless charging: Infineon and PowerElectronics.com Available online: https://www.powerelectronics.com/markets/automotive/article/21 864097/wireless-charging-of-electric-vehicles accessed on 9 December 2021), and (**b**) the proposed non-invasive on-chip sensing system.
