Frequency-Domain Power Delivery Network Self-Characterization in FPGAs for Improved System Reliability

Zhao, Shuze; Ahmed, Ibrahim; Betz, Vaughn; Lotfi, A.; Trescases, Olivier

doi:10.1109/tie.2018.2808907

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…The contribution of each individual component to the PDN's impedance is distinct at diferent frequencies. While in lower frequencies, the equivalent impedance of the PDN is dominated by the voltage regulator's characteristics, in higher frequencies, the of-chip and on-chip components are contributing most to the impedance [25,53,54].…”

Section: Power Distribution Network (Pdn) Characterizationmentioning

confidence: 99%

ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network

Mosavirik

Ganji

Schaumont

et al. 2022

J. Emerg. Technol. Comput. Syst.

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The globalization of electronic systems’ fabrication has made some of our most critical systems vulnerable to supply chain attacks. Implanting spy chips on the printed circuit boards (PCBs) or replacing genuine components with counterfeit/recycled ones are examples of such attacks. Unfortunately, conventional attack detection schemes for PCBs are ad-hoc, costly, unscalable, and error-prone. This work introduces a holistic physical verification framework for PCBs, called ScatterVerif , based on the characterization of the PCBs’ power distribution network (PDN). First, we demonstrate how scattering parameters, frequently used for impedance characterization of RF circuits, can characterize the entire PCB with a single measurement. Second, we present how a class of machine learning algorithms, namely the Gaussian Mixture Model, can be applied to the measurements to automatically classify/cluster the genuine and tampered/counterfeit PCBs. We show that these attacks affect the overall impedance of a PCB differently in various frequency ranges , and hence, the conventional impedance measurements using a constant-frequency electrical stimulus might leave the attack undetected. We conduct extensive experiments on counterfeit and tampered devices and demonstrate that these attacks can be detected with a high-confidence. Finally, we show that the acquired data from the PDN characterization can also be deployed for fingerprinting genuine PCBs.

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Section: Power Distribution Network (Pdn) Characterizationmentioning

confidence: 99%

ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network

Mosavirik

Ganji

Schaumont

et al. 2022

J. Emerg. Technol. Comput. Syst.

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“…Zhao et al [22] tackled the IR-drop error through polarization potential test, theoretically derived the double reference electrode method to eliminate the IR-drop, and experimentally proved that the method can eliminate the IR-drop, which exists in the stray current monitoring of the URT.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Simulation of an Intelligent Current Monitoring System for Urban Rail Transit

Yao

Zhao

et al. 2020

IEEE Access

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Many urban rail transit (URT) systems adopt the DC traction power supply system. Because of the impedance and incomplete ground insulation of the running track, it is inevitable for a part of the traction current to flow into the ground from the track, creating the stray current. This type of current causes great safety hazards to the metal structures in and near the URT system. Considering the power supply mode of the URT, this paper explores the different resistances in each power supply section under unilateral power supply and bilateral power supply. Then, the defects of the current discharge method were identified in the context of stray current protection. To solve these defects, the backpropagation neural network (BPNN) was adopted to build a discharge flow prediction model. On this basis, an intelligent current monitoring system was established for the URT. Finally, the authors simulated the impact of each factor on stray current, and verified the reliability and stability of the proposed monitoring system. Compared with predicted values and the actual values, the prediction agrees with the actual data very well. INDEX TERMS Urban rail transit (URT), backpropagation neural network (BPNN), stray current, current monitoring system.

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PowerScout: A Security-Oriented Power Delivery Network Modeling Framework for Cross-Domain Side-Channel Analysis

Zhu

Guo

Jin

et al. 2020

2020 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

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Frequency-Domain Power Delivery Network Self-Characterization in FPGAs for Improved System Reliability

Cited by 9 publications

References 29 publications

ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network

ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network

Design and Simulation of an Intelligent Current Monitoring System for Urban Rail Transit

PowerScout: A Security-Oriented Power Delivery Network Modeling Framework for Cross-Domain Side-Channel Analysis

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