Detecting Hardware Trojans Inserted by Untrusted Foundry Using Physical Inspection and Advanced Image Processing

Vashistha, Nidish; Rahman, Mir Tanjidur; Shen, Haoting; Asadizanjani, Navid; Tehranipoor, Mark

doi:10.1007/s41635-018-0055-0

Cited by 36 publications

(13 citation statements)

References 29 publications

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“…LASRE is another technique that relies on using frequencybased texture signatures for different materials to segment out IC structure across multiple layers [63]. Simple image processing techniques, such as Otsu's binarization, have also been explored for segmenting SEM images [80]- [84].…”

Section: A Data-driven Approaches In Rementioning

confidence: 99%

“…Being in the middle of the RE workflow, the amount of errors accumulated at this stage is far lower than further down the line. Several approaches use segmented images of the IC layers for detecting hardware Trojans [6], [84], [89]- [93]. Being segmented, hardware assurance measures performed at this stage can also be free of the influence of localized variation in pixel intensity and noise.…”

Section: Vectorizationmentioning

confidence: 99%

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REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

Wilson¹,

Zhu

et al. 2021

IEEE Access

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Comprehensive hardware assurance approaches guaranteeing trust on Integrated Circuits (ICs) typically require the verification of the IC design layout and functionality through destructive Reverse Engineering (RE). It is a resource intensive process that will benefit greatly from the extensive integration of data-driven paradigms, especially in the imaging and image analysis phase. Although obvious, this uptake of data-driven approaches into RE-assisted hardware assurance is lagging due to the lack of massive amounts of high-quality labelled data. In this paper, a large-scale synthetic Scanning Electron Microscopy (SEM) dataset, REFICS, is introduced to address this issue. The dataset, the first open-source dataset in the RE community, consists of 800,000 SEM images over two node technologies, 32nm and 90nm, and four cardinal layers of the IC, namely, doping, polysilicon, contact and metal layers. Furthermore, a framework, based on uncertainty and risk, is introduced to compare the efficacy and benefits of existing RE workflows utilizing ad-hoc steps in its execution. These developments are critical in developing RE-assisted hardware assurance into a scalable, automated and fault-tolerant approach. Finally, the work is concluded with the performance analysis of existing machine learning and deep learning approaches for image analysis in RE and hardware assurance.

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Section: A Data-driven Approaches In Rementioning

confidence: 99%

Section: Vectorizationmentioning

confidence: 99%

REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

Wilson¹,

Zhu

et al. 2021

IEEE Access

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“…A SEM-based acquisition technique, named SEMBA, for white team HTs detection is proposed [14], which is based on the wet etching, scanning electron microscopy and multiple image alignment. A computer vision-based framework for the HT detection on golden IC and IUA images is designed [15]. A HTs detection method named Golden Gates is designed to achieve a comparable level of accuracy to reverse engineering only with less time cost [16].…”

Section: Introductionmentioning

confidence: 99%

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

et al. 2022

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Verification is one of the core steps in integrated circuits (ICs) manufacturing due to the multifarious defects and malicious hardware Trojans (HTs). In most cases, the effectiveness of the detection relies on the quality of the sample images of ICs. However, the high-precision and noiseless images are hard to capture due to the mechanical precision, manual error and environmental interference. In this paper, an effective approach for processing the low-quality image data of ICs is proposed. Our approach can successfully categorize the partial pictures of multiple objected ICs with low resolution and various noise. The proposed approach extracts the high-frequency texture components (HFTC) of the images and constructs a graph with the correlationship among features. Subsequently, the spectral clustering is conducted for obtaining the final cluster indicators. The low-quality images of ICs can be successfully categorized by the proposed approach, which will provide a data foundation for the following verification tasks. In order to evaluate the effectiveness of the proposed approach, several experiments are conducted in the simulated datasets, which are generated by corrupting the real-world data in different conditions. The clustering results reveal that our approach can achieve the best performance with good stability compared to the baselines.

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“…This information can then be used to dynamically change the imaging parameters, as necessary, for different regions of the sample rather than using a constant setting for the entire sample. Since low quality imaging takes significantly less time than high quality imaging [3], the time required to image a sample would only be constrained only by the high frequency regions present in the sample. We were able to reduce the imaging time by 20% for the 130nm node technology IC shown in Fig.…”

mentioning

confidence: 99%

“…However, RE requires long hours of imaging for an IC even if it is done automatically. In [3], the estimated time to image an IC of size 1.5mm x 1.5mm employing a 130nm node technology with high resolution went up to 30 days. With the ongoing trend of adding more structures onto a limited space on the IC, the imaging time frame is becoming unfeasible.…”

mentioning

confidence: 99%

First Auto-Magnifier Platform for Hardware Assurance and Reverse Engineering Integrated Circuits

2019

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Scanning Electron Microscopy (SEM) is a common imaging modality used in the semiconductor industry particularly for failure analysis [1, 2]. In addition, with the recent growing concern of physical attacks on electronics with malicious intent, SEMs are becoming more attractive for hardware assurance and especially, Reverse Engineering (RE) [4]. However, RE requires long hours of imaging for an IC even if it is done automatically. In [3], the estimated time to image an IC of size 1.5mm x 1.5mm employing a 130nm node technology with high resolution went up to 30 days. With the ongoing trend of adding more structures onto a limited space on the IC, the imaging time frame is becoming unfeasible. This drawback would be manageable in situations where there is a prior knowledge on the exact location to be imaged. However, in case of hardware assurance and reverse engineering, where all the structures in the entire IC need to be extracted precisely without any prior knowledge, the drawback would be a major limiting factor. There are several parameters in the SEM imaging modality such as magnification that can be adjusted to reduce the time requirements. In [4], Principe et al. even introduced a platform for automated processing for an IC. However, both approaches do not consider the density of the structures being imaged and fails to investigate if the region warrants a high definition setting to resolve the features satisfactorily. In this paper, we introduce a new algorithm that can quantify the density of objects in a region using a fast-acquired low magnification image and define the right value of magnification for imaging different regions automatically. Once the density of the objects is quantified the microscope can increase the magnification with a predefined step (10X, 100X, 1000X, etc.) and collect new images but only from the region detected as a dense region. This loop will be repeated until the whole area is imaged with the right magnification. The images are assumed to be analyzed using advanced image analysis methods incorporated with machine learning and deep learning algorithms for hardware assurance. Therefore, as the image analysis algorithms are further developed a lower magnified image might be also acceptable for analysis. This will be used towards building an intelligent microscope that can considerably reduce the imaging time requirement which would greatly facilitate efficient hardware assurance and RE.

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Detecting Hardware Trojans Inserted by Untrusted Foundry Using Physical Inspection and Advanced Image Processing

Cited by 36 publications

References 29 publications

REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

First Auto-Magnifier Platform for Hardware Assurance and Reverse Engineering Integrated Circuits

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