High-resolution Terahertz near-field reflection measurements for complementary non-destructive inspection of integrated circuits

Michalski, Alexander; Sawallich, Simon; True, John; Asadizanjani, Navid; Nagel, M.

doi:10.1109/irmmw-thz50926.2021.9567363

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…27 In this paper, a TeraCube Scientific THz near-field scanning system is used to collect the THz-TDS signal from the IC samples. 28 This system utilizes photoconductive antennas (PCAs) to both generate and detect broadband THz pulses. Figure .2 shows a schematic of this system.…”

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

View full text Add to dashboard Cite

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“…Additionally, the application of a deconvolution algorithm significantly enhances signal processing. 26 This not only denoises the signal but also aids in more easily and clearly identifying the time delay of the signal, leading to more accurate thickness characterization.The ability to measure the thickness of buried samples has allowed this method to generate unique IDs for tracking and tracing purposes. 27 Decoupling the thickness and refractive index of the THz-TDS thickness characterization has also emerged as a attractive area of research.…”

Section: Materials and Structure Measurementmentioning

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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