503 research outputs found

    AI/ML Algorithms and Applications in VLSI Design and Technology

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    An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

    Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

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    The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation

    Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

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    The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation

    Investigation into Detection of Hardware Trojans on Printed Circuit Boards

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    The modern semiconductor device manufacturing flow is becoming increasingly vulnerable to malicious implants called Hardware Trojans (HT). With HTs becoming stealthier, a need for more accurate and efficient detection methods is becoming increasingly crucial at both Integrated Circuit (IC) and Printed Circuit Board (PCB) levels. While HT detection at an IC level has been widely studied, there is still very limited research on detecting and preventing HTs implanted on PCBs. In recent years the rise of outsourcing design and fabrication of electronics, including PCBs, to third parties has dramatically increased the possibility of malicious alteration and consequently the security risk for systems incorporating PCBs. Providing mechanical support for the electrical interconnections between different components, PCBs are an important part of electronic systems. Modern, complex and highly integrated designs may contain up to thirty layers, with concealed micro-vias and embedded passive components. An adversary can aim to modify the PCB design by tampering the copper interconnections or inserting extra components in an internal layer of a multi-layer board. Similar to its IC counterpart, a PCB HT can, among other things, cause system failure or leakage of private information. The disruptive actions of a carefully designed HT attack can have catastrophic implications and should therefore be taken seriously by industry, academia and the government. This thesis gives an account of work carried out in three projects concerned with HT detection on a PCB. In the first contribution a power analysis method is proposed for detecting HT components, implanted on the surface or otherwise, consuming power from the power distribution network. The assumption is that any HT device actively tampering or eavesdropping on the signals in the PCB circuit will consume electrical power. Harvesting this side-channel effect and observing the fluctuations of power consumption on the PCB power distribution network enables evincing the HT. Using a purpose-built PCB prototype, an experimental setup is developed for verification of the methodology. The results confirm the ability to detect alien components on a PCB without interference with its main functionality. In the second contribution the monitoring methodology is further developed by applying machine learning (ML) techniques to detect stealthier HTs, consuming power from I/O ports of legitimate ICs on the PCB. Two algorithms, One-Class Support Vector Machine (SVM) and Local Outlier Factor (LOF), are implemented on the legitimate power consumption data harvested experimentally from the PCB prototype. Simulation results are validated through real-life measurements and experiments are carried out on the prototype PCB. For validation of the ML classification models, one hundred categories of HTs are modelled and inserted into the datasets. Simulation results show that using the proposed methodology an HT can be detected with high prediction accuracy (F1-score at 99% for a 15 mW HT). Further, the developed ML model is uploaded to the prototype PCB for experimental validation. The results show consistency between simulations and experiments, with an average discrepancy of ±5.9% observed between One-Class SVM simulations and real-life experiments. The machine learning models developed for HT detection are low-cost in terms of memory (around 27 KB). In the third contribution an automated visual inspection methodology is proposed for detecting HTs on the surface of a PCB. It is based on a combination of conventional computer vision techniques and a dual tower Siamese Neural Network (SNN), modelled in a three stage pipeline. In the interest of making the proposed methodology broadly applicable a particular emphasis is made on the imaging modality of choice, whereby a regular digital optical camera is chosen. The dataset of PCB images is developed in a controlled environment of a photographic tent. The novelty in this work is that, instead of a generic production fault detection, the algorithm is optimised and trained specifically for implanted HT component detection on a PCB, be it active or passive. The proposed HT detection methodology is trained and tested with three groups of HTs, categorised based on their surface area, ranging from 4 mm² to 280 mm² and above. The results show that it is possible to reach effective detection accuracy of 95.1% for HTs as small as 4 mm². In case of HTs with surface area larger than 280 mm² the detection accuracy is around 96.1%, while the average performance across all HT groups is 95.6%

    Integration of Spectroscopic and Mass Spectrometric Tools for the Analysis of Novel Psychoactive Substances in Forensic and Toxicology Applications

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    Analytical methods aiming for the detection of novel psychoactive substances are continuously revised due to their utility in the seized drug and toxicology realms. One method frequently employed for the preliminary identification of illicit materials is portable Raman spectroscopy. Even when a substance in possession of an offender is identified, conclusive evidence that it may have been consumed requires additional confirmatory work and further toxicological evaluation of a biological specimen. Many times, the substance consumed may not be detected in the analyzed specimen due to its extensive metabolism. It is therefore challenging to rule out the identity of the drug ingested if metabolic studies have not been performed on a particular substance. This research aims to evaluate portable Raman as a quick, safe, non-destructive method for drug analysis using the instrument’s built-in algorithms and in-house machine and deep learning algorithms. Furthermore, metabolic and toxicologic studies using zebrafish and human liver microsomes are used to elucidate selected opioids. In the first part of this research, a portable Raman instrument—TacticID was validated according to the United Nations Office on Drugs and Crime guidelines using 14 drugs and 15 cutting agents commonly encountered in seized drugs. Analysis was performed through glass and plastic packaging. In-house binary mixtures (n = 64) at the following ratios—1:4, 1:7, 1:10, and 1:20 were evaluated and the results compared to direct analysis in real-time mass spectrometry (DART-MS). Whereas Raman performed better at detecting diluents which consisted of the majority in the mixtures, DART-MS resulted in higher identification for easily ionizable drugs which were present in lower percentages. To compliment the weaknesses in each technique, both methods were combined, resulting in 96% accuracy. However, analysis of 15 authentic adjudicated cases resulted in 83% accuracy using the combined methods, demonstrating the usefulness of these methods as preliminary tests over traditional subjective techniques such as color tests. In instances where a portable Raman instrument is used for drug screening, its accuracy as a single technique is crucial. In this study, the correct identification of the instrument detecting both drug and diluent in binary mixtures was 19%. Therefore, machine learning methods were explored as alternatives to the instrument’s built-in hit quality index algorithm. The findings in this research demonstrated that neural networks and convolutional neural networks were superior to the other algorithms, increasing the correct identification of both compounds to 65 and 64%, respectively. This work demonstrated how the contribution of machine learning can help improve the accuracy of analytical instruments outputs thereby increasing confidence in compounds reported. In the second part of this research, zebrafish which share 70% of gene similarity to humans, were used as a toxicity model to provide information about drug effects on a living system. Fentanyl was selected as a model drug and zebrafish (0 – 96 hours post fertilization) were dosed at 0.01 – 100 µM. Major dose dependent phenotypic effects included pericardial malformations, spine, and yolk extension malformation, all of which inhibited the normal growth and development of the larvae. Additionally, the metabolism of fentanyl and valerylfentanyl were elucidated using zebrafish. Therefore, this work provided insight into the zebrafish model as an alternative to human toxicity and metabolism. The knowledge gained through this research will be used to understand the mechanisms by which these toxic and metabolic effects are observed

    BagStack Classification for Data Imbalance Problems with Application to Defect Detection and Labeling in Semiconductor Units

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    abstract: Despite the fact that machine learning supports the development of computer vision applications by shortening the development cycle, finding a general learning algorithm that solves a wide range of applications is still bounded by the ”no free lunch theorem”. The search for the right algorithm to solve a specific problem is driven by the problem itself, the data availability and many other requirements. Automated visual inspection (AVI) systems represent a major part of these challenging computer vision applications. They are gaining growing interest in the manufacturing industry to detect defective products and keep these from reaching customers. The process of defect detection and classification in semiconductor units is challenging due to different acceptable variations that the manufacturing process introduces. Other variations are also typically introduced when using optical inspection systems due to changes in lighting conditions and misalignment of the imaged units, which makes the defect detection process more challenging. In this thesis, a BagStack classification framework is proposed, which makes use of stacking and bagging concepts to handle both variance and bias errors. The classifier is designed to handle the data imbalance and overfitting problems by adaptively transforming the multi-class classification problem into multiple binary classification problems, applying a bagging approach to train a set of base learners for each specific problem, adaptively specifying the number of base learners assigned to each problem, adaptively specifying the number of samples to use from each class, applying a novel data-imbalance aware cross-validation technique to generate the meta-data while taking into account the data imbalance problem at the meta-data level and, finally, using a multi-response random forest regression classifier as a meta-classifier. The BagStack classifier makes use of multiple features to solve the defect classification problem. In order to detect defects, a locally adaptive statistical background modeling is proposed. The proposed BagStack classifier outperforms state-of-the-art image classification techniques on our dataset in terms of overall classification accuracy and average per-class classification accuracy. The proposed detection method achieves high performance on the considered dataset in terms of recall and precision.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

    OCM 2021 - Optical Characterization of Materials : Conference Proceedings

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    The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field
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