Histogram-based Auto Segmentation: A Novel Approach to Segmenting Integrated Circuit Structures from SEM Images

In the Reverse Engineering and Hardware Assurance domain, a majority of the data acquisition is done through electron microscopy techniques such as Scanning Electron Microscopy (SEM). However, unlike its counterparts in optical imaging, only a limited number of techniques are available to enhance and extract information from the raw SEM images. In this paper, we introduce an algorithm to segment out Integrated Circuit (IC) structures from the SEM image. Unlike existing algorithms discussed in this paper, this algorithm is unsupervised, parameter-free and does not require prior information on the noise model or features in the target image making it effective in low quality image acquisition scenarios as well. Furthermore, the results from the application of the algorithm on various structures and layers in the IC are reported and discussed

Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives

In the context of hardware trust and assurance, reverse engineering has been often considered as an illegal action. Generally speaking, reverse engineering aims to retrieve information from a product, i.e., integrated circuits (ICs) and printed circuit boards (PCBs) in hardware security-related scenarios, in the hope of understanding the functionality of the device and determining its constituent components. Hence, it can raise serious issues concerning Intellectual Property (IP) infringement, the (in)effectiveness of security-related measures, and even new opportunities for injecting hardware Trojans. Ironically, reverse engineering can enable IP owners to verify and validate the design. Nevertheless, this cannot be achieved without overcoming numerous obstacles that limit successful outcomes of the reverse engineering process. This paper surveys these challenges from two complementary perspectives: image processing and machine learning. These two fields of study form a firm basis for the enhancement of efficiency and accuracy of reverse engineering processes for both PCBs and ICs. In summary, therefore, this paper presents a roadmap indicating clearly the actions to be taken to fulfill hardware trust and assurance objectives.Comment: It is essential not to reduce the size of the figures as high quality ones are required to discuss the image processing algorithms and method

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

DEVELOPING A NEW APPROACH TO CHANGE AND LEARNING IN PUBLIC SECTOR ORGANISATIONS

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