Machine Learning Assisted Counterfeit IC Detection through Non-destructive Infrared (IR) Spectroscopy Material Characterization

Xi, Chengjie; Jessurun, Nathan; True, John; Khan, Aslam A.; Tehranipoor, Mohammad; Asadizanjani, Navid

doi:10.1109/ectc51906.2022.00355

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Previous research has observed material differences between authentic and counterfeit integrated circuit (IC) samples using non-destructive Fourier Transform Infrared (FTIR) material characterization. 26 Additionally, farfield THz-TDS has successfully detected variations in the refractive index between authentic and counterfeit IC samples. 27 In this paper, a TeraCube Scientific THz near-field scanning system is used to collect the THz-TDS signal from the IC samples.…”

Section: Near-field Thz-tds For Ic Packaging Characterizationmentioning

confidence: 99%

Enhancing counterfeit detection of integrated circuits through machine learning-assisted THz-TDS analysis

Xi,

Varshney,

Khan

et al. 2024

Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XVII

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Due to the global offshore fabrication of semiconductors, hardware security problems such as counterfeit ICs and Hardware Trojans (HTs) have affected semiconductor device trustworthiness in critical applications. Previous research has proven the encapsulant material difference exists in the counterfeit ICs. What's more by using a pulsed THz signal, Terahertz Time-Domain Spectroscopy (THz-TDS) is able to detect the effective refractive index difference between authentic and counterfeit IC packaging. This research has also successfully observed the reflective index difference between authentic and counterfeit by measuring the layer thickness and THz-TDS time delay. However, the accuracy of calculating the effective refractive index depends on the accuracy of the layer thickness and the time delay measurements. Consequently, reflective index difference may arise from noise encountered during data collection, which can affect the accuracy and repeatability of counterfeit detection tasks. In this paper, we utilize an unsupervised machine learning model to further demonstrate the capabilities of THz-TDS in counterfeit IC detection. Additionally, the potential of using THz-TDS to generate a unique fingerprint is also discussed.

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Section: Near-field Thz-tds For Ic Packaging Characterizationmentioning

confidence: 99%

Enhancing counterfeit detection of integrated circuits through machine learning-assisted THz-TDS analysis

Xi,

Varshney,

Khan

et al. 2024

Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XVII

Self Cite

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“…The EM emissions from clock distribution mechanism are used as fingerprints to detect counterfeit ICs. Furthermore material based characterization to train the classification models, which can provide interpretability and hardware assurance capabilities Chengjie et al [7]. These advanced techniques are limited with the requirement with the limited availability of data for the training process, which leads to less accurate models and degraded classification rates.…”

Section: Related Workmentioning

confidence: 99%

AutoDetect: Novel Autoencoding Architecture for Counterfeit IC Detection

Bhure,

Nicholas,

Ghosh

et al. 2023

Preprint

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The global electronic supply chain industry is a complex and interconnected network of companies, organizations, and individuals that collaborate to produce and distribute electronic devices and components. It plays a critical role in the global economy, as electronic devices have become an essential part of modern life. Electronic components and devices are produced and distributed across the world, with different regions and countries specializing in different aspects of the supply chain. This global nature of the electronic supply chain industry also poses challenges, particularly in terms of quality control and supply chain transparency involving counterfeit components owing to its interconnected nature. Counterfeiting of integrated circuits (ICs) and semiconductor devices is a significant challenge that poses threats to the safety and reliability of electronic devices, the global economy, intellectual property rights, and the overall sustainability of the electronic supply chain industry. To address this challenge, we propose a novel autoencoding architecture for counterfeit IC detection using a labeled database. The proposed architecture achieves an overall accuracy of 83% which is approximately ≈ 20% more than the existing transfer learning approaches.

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“…Consequently, these variations can be analyzed to distinguish authentic components from counterfeit ones. Numerous non-destructive physical inspection techniques have been developed to detect and monitor variances in the material and structure of IC packaging as shown in Figure . 1, including X-ray computed tomography (CT) 7,8 ( 1(a)),Fourier Transform Infrared Spectroscopy (FTIR) 9 ( 1(b)), and Scanning Acoustic Microscopy (SAM) 10 (1(c)& (d)), etc.…”

Section: Introductionmentioning

confidence: 99%

THz-TDS for IC packaging material changes detection under real-world conditions

Xi,

Varshney,

Khan

et al. 2024

Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XVII

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Hardware security has been a significant challenge in semiconductor devices, leading to serious issues such as denial of service, data leakage etc at both transistor as well as package level. Detecting the usage state of a target semiconductor sample or generating a unique fingerprint can provide critical information about the sample, aiding in the prevention of counterfeiting to avoid hardware security risks. Terahertz Time-Domain Spectroscopy (THz-TDS) stands out as a powerful and non-destructive tool, capable of probing and analyzing the polymer materials. Its sensitivity to molecular-level changes makes THz-TDS suitable for studying the material, structural, and stress properties of IC packaging under real-world conditions. This research begins by reviewing the composition of Electronic Material Compatibility (EMC) materials and their variance under real-world conditions. Furthermore, the application of THz-TDS for polymer material characterization, which incloudes material, structural, and internal stress characterization, is also thoroughly reviewed. By combining these factors, we explore the potential and capability of using THz-TDS to detect real-world loaded samples and the feasibility of generating fingerprints for identifying counterfeit samples, including reused, recycled, or tampered ones.

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Machine Learning Assisted Counterfeit IC Detection through Non-destructive Infrared (IR) Spectroscopy Material Characterization

Cited by 7 publications

References 17 publications

Enhancing counterfeit detection of integrated circuits through machine learning-assisted THz-TDS analysis

Enhancing counterfeit detection of integrated circuits through machine learning-assisted THz-TDS analysis

AutoDetect: Novel Autoencoding Architecture for Counterfeit IC Detection

THz-TDS for IC packaging material changes detection under real-world conditions

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