Hybrid &lt;inline-formula&gt; 
                     &lt;tex-math notation="LaTeX"&gt;${K}$ &lt;/tex-math&gt;
                  &lt;/inline-formula&gt;-Means Clustering and Support Vector Machine Method for via and Metal Line Detections in Delayered IC Images

Cheng, Deruo; Shi, Yong; Lin, Tong; Gwee, Bah-Hwee; Toh, Kar-Ann

doi:10.1109/tcsii.2018.2827044

Cited by 24 publications

(6 citation statements)

References 15 publications

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“…Recently, Machine Learning (ML) and Deep Learning (DL) based approaches have gained increasing attention in image analysis for IC images [1]. Cheng et al [8] proposed a combined process using K-Means Clustering and Support As a result, the challenges in image analysis for IC images are largely addressed. However, recently proposed automation techniques stop at the standard cell recognition process.…”

Section: B Image Processing For Sem Imagesmentioning

confidence: 99%

“…Standard cell localization and recognition is the most important step because the reliable extraction of netlist from SEM images is a primary prerequisite for the success and accuracy of subsequent netlist analysis in the process of HA [4]- [7]. Localization of standard cells in SEM images has been well studied [8]- [10], however, standard cell recognition remains a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

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GRACER: Graph-Based Standard Cell Recognition in IC Images for Hardware Assurance

Huang,

Hong,

Lin

et al. 2023

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

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Global distribution of the Integrated Circuit (IC) supply chain amplifies the importance of Hardware Assurance (HA), i.e., to ensure the integrity of manufactured IC. Standard cell recognition is a crucial step in HA, which is to identify the functionality of a standard cell based on its Scanning Electron Microscope (SEM) images. Conventionally, this is mostly done by human inspection, which is labor-intensive and error-prone. Current works on automating this process only work on the image domain and have sub-optimal performance due to the challenges incurred by the variation in the appearance of standard cells in the images. In this paper, we propose an automatic process for standard cell recognition, through conversion to a standardized graph representation and comparing the graph structure to identify the type of the standard cell. Our proposed method represents each unique circuit structure in a unique graph representation and thus enables a one-to-one matching to a known set of templates for functionality identification. Our experiments show that our proposed method can always recognize the standard cells correctly, even under the most challenging scenario.

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Section: B Image Processing For Sem Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GRACER: Graph-Based Standard Cell Recognition in IC Images for Hardware Assurance

Huang,

Hong,

Lin

et al. 2023

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

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“…SVM is used for solving classification and regression tasks, primarily mapping data samples into a feature space through a kernel function and subsequently classifying them using hyperplane [17]. It is divided into kernel and simple SVM, which is commonly used for solving classification problems can be seen in Formula 6.…”

Section: K-nearest Neighbormentioning

confidence: 99%

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Basuni,

Amril Mutoi Siregar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Cases of drug abuse are on the rise, with many users entering the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consists of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1-score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% accuracy, 87% precision, 82% recall, 84% f1-score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and non-users.

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“…For example, the authors of [11] published a fully convolutional network with VGG-16 encoder for segmentation of metal tracks and VIAs. In [4], the authors postulate an unsupervised K-means approach to the same task, but they acknowledge that this method is severely limited due to preparation shortcomings and image variations. The development of applied methods for counterfeit detection has yet been exclusively limited to package analyses [1], [6], [10].…”

Section: Related Workmentioning

confidence: 99%

Boundary Enhanced Semantic Segmentation for High Resolution Electron Microscope Images

Pollach¹,

Schiegg²,

Ludwig³

et al. 2022

2022 30th European Signal Processing Conference (EUSIPCO)

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This work proposes an automated semantic segmentation approach for high resolution scanning electron microscope images, which enables the detection of hardware Trojans and counterfeit integrated circuits. We evaluate state of the art segmentation approaches and leverage expert domain knowledge to propose a neural network architecture tailored for our use case. We further address the challenge of the limited availability of training images and evaluate which pre-trained encoder can be leveraged most effectively for the given use case. The proposed segmentation network uses expert domain knowledge to account for the importance of separating technology features on a finegrain level by introducing a separate boundary stream. The test results compare our network to a baseline approach and to two state-of-the-art segmentation networks.

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Hybrid <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math> </inline-formula>-Means Clustering and Support Vector Machine Method for via and Metal Line Detections in Delayered IC Images

Abstract: Hybrid K -means clustering and support vector machine method for via and metal line detections in delayered IC images

Cited by 24 publications

References 15 publications

GRACER: Graph-Based Standard Cell Recognition in IC Images for Hardware Assurance

GRACER: Graph-Based Standard Cell Recognition in IC Images for Hardware Assurance

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Boundary Enhanced Semantic Segmentation for High Resolution Electron Microscope Images

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