404 research outputs found

    Crystal Structures of Metal Complexes

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    This reprint contains 11 papers published in a Special Issue of Molecules entitled "Crystal Structures of Metal Complexes". I will be very happy if readers will be interested in the crystal structures of metal complexes

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Imaging Probe for Charged Particle Detection

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    Single Photon Avalanche Diodes (SPADs) are semiconductor devices that detect individual photons. However, they can also experience dark count rate (DCR), generating avalanche current even when no photons are present, which limits their ability to detect low-level signals. SPADs characterization is important to gain insight into their behavior and improve their performance for various applications. This thesis discusses the development of a portable detection probe that uses the APIX2LF chip, which contains arrays of SPADs that were produced using a 150 nm standard CMOS process. A prototype board, that includes a battery, front-end electronics, and a microcontroller acting as the interface between the sensor and the PC was developed and tested using a beta-emitting source. Additionally, custom firmware was designed for the microcontroller and an automatic data acquisition framework was developed for the characterization of the DCR of six APIX2LF chips at different bias voltages and temperatures.This thesis discusses the development of a portable detection probe that uses the APIX2LF chip, which contains arrays of SPADs that were produced using a 150 nm standard CMOS process. A prototype board, that includes a battery, front-end electronics, and a microcontroller acting as the interface between the sensor and the PC was developed and tested using a beta-emitting source. Additionally, custom firmware was designed for the microcontroller and an automatic data acquisition framework was developed for the characterization of the DCR of six APIX2LF chips at different bias voltages and temperatures

    Substoichiometric Phases of Hafnium Oxide with Semiconducting Properties

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    Since the dawn of the information age, all developments that provided a significant improvement in information processing and data transmission have been considered as key technologies. The impact of ever new data processing innovations on the economy and almost all areas of our daily lives is unprecedented and a departure from this trend is unimaginable in the near future. Even though the end of Moore's Law has been predicted all too often, the steady exponential growth of computing capacity remains unaffected to this day, due to tremendous commercial pressure. While the minimum physical size of the transistor architecture is a serious constraint, the steady evolution of computing effectiveness is not limited in the predictable future. However, the focus of development will have to expand more strongly to other technological aspects of information processing. For example, the development of new computer paradigms which mark a departure from the digitally dominated van Neumann architecture will play an increasingly significant role. The category of so-called next-generation non-volatile memory technologies, based on various physical principles such as phase transformation, magnetic or ferroelectric properties or ion diffusion, could play a central role here. These memory technologies promise in part strongly pronounced multi-bit properties up to quasi-analog switching behavior. These attributes are of fundamental importance especially for new promising concepts of information processing like in-memory computing and neuromorphic processing. In addition, many next-generation non-volatile memory technologies already show advantages over conventional media such as Flash memory. For example, their application promises significantly reduced energy consumption and their write and especially read speeds are in some cases far superior to conventional technology and could therefore already contribute significant technological improvements to the existing memory hierarchy. However, these alternative concepts are currently still limited in terms of their statistical reliability, among other things. Even though phase change memory in the form of the 3D XPoint, for example, has already been commercialized, the developments have not yet been able to compete due to the enormous commercial pressure in Flash memory research. Nevertheless, the further development of alternative concepts for the next and beyond memory generations is essential and the in-depth research on next-generation non-volatile memory technologies is therefore a hot and extremely important scientific topic. This work focuses on hafnium oxide, a key material in next-generation non-volatile memory research. Hafnium oxide is very well known in the semiconductor industry, as it generated a lot of attention in the course of high-k research due to its excellent dielectric properties and established CMOS compatibility. However, since the growing interest in so-called memristive memory, research efforts have primarily focused on the value of hafnium oxide in the form of resistive random-access memory (RRAM) and, with the discovery of ferroelectricity in HfO₂, ferroelectric resistive random-access memory (FeRAM). RRAM is a next-generation non-volatile memory technology that features a simple metal-insulator-metal (MIM) structure, excellent scalability, and potential 3D integration. In particular, the aforementioned gradual to quasi-continuous switching behavior has been demonstrated on a variety of RRAM systems. A significant change of the switching properties is achievable, for example, by the choice of top and bottom electrodes, the introduction of doping elements, or by designated oxygen deficiency. In particular, the last point is based on the basic physical principle of the hafnium oxide-based RRAM mechanism, in which local oxygen ions are stimulated to diffuse by applying an electrical potential, and a so-called conducting filament is formed by the remaining vacancies, which electrically connects the two electrode sides. The process is characterized by the reversibility of the conducting filament which can be dissolved by a suitable I-V programming (e.g., reversal of the voltage direction). In the literature there are some predictions of sub-stoichiometric hafnium oxide phases, such as Hf₂O₃, HfO or Hf₆O, which could be considered as conducting filament phases, but there is a lack of conclusive experimental results. While there are studies that assign supposed structures in oxygen-deficient hafnium oxide thin films, these assignments are mostly based on references from various stoichiometric hafnium oxide high-temperature phases such as tetragonal t-HfO₂ (P4₂/nmc) or cubic c-HfO₂ (Fm-3m), or high-pressure phases such as orthorhombic o-HfO₂ (Pbca). Furthermore, the structural identification of such thin films proves to be difficult, as they are susceptible to arbitrary texturing and reflection broadening in X-ray diffraction. In addition, such thin films are usually synthesized as phase mixtures with monoclinic hafnium oxide. A further challenge in property determination is given by their usual arrangement in MIM configuration, which is determined by the quality of top and bottom electrodes and their interfaces to the active material. It is therefore a non-trivial task to draw conclusions on individual material properties such as electrical conductivity in such (e.g., oxygen-deficient) RRAM devices. To answer these open questions, this work is primarily devoted to material properties of oxygen-deficient hafnium oxide phases. Therefore, in the first comprehensive study of this work, Molecular-Beam Epitaxy (MBE) was used to synthesize hafnium oxide phases over a wide oxidation range from monoclinic to hexagonal hafnium oxide. The hafnium oxide films were deposited on c-cut sapphire to achieve effective phase selection and identification by epitaxial growth, taking into account the position of relative lattice planes. In addition, the choice of a substrate with a high band gap and optical transparency enabled the direct investigation of both optical and electrical properties by means of UV/Vis transmission spectroscopy and Hall effect measurements. With additional measurements via X-ray diffraction (XRD), X-ray reflectometry (XRR), X-ray photoelectron spectroscopy (XPS) and high-resolution transmission electron microscopy (HRTEM), the oxygen content-dependent changes in crystal as well as band structure could be correlated with electrical properties. Based on these results, a comprehensive band structure model over the entire oxidation range from insulating HfO₂ to metallic Hf was established, highlighting the discovered intermediate key structures of rhombohedral r-HfO₁.₇ and hexagonal hcp-HfO₀.₇. In the second topic of this work, the phase transition from stoichiometric monoclinic to oxygen-deficient rhombohedral hafnium oxide was complemented by DFT calculations in collaboration with the theory group of Prof. Valentí (Frankfurt am Main). A detailed comparison between experimental results and DFT calculations confirms previously assumed mechanisms for phase stabilization. In addition, the comparison shows a remarkable agreement between experimental and theoretical results on the crystal- and band stucture. The calculations allowed to predict the positions of oxygen ions in oxygen-deficient hafnium oxide as well as the associated space group. Also, the investigations provide information on the thermodynamic stability of the corresponding phases. Finally, the orbital-resolved hybridization of valence states influenced by oxygen vacancies is discussed. Another experimental study deals with the reproduction and investigation, of the aforementioned substoichiometric hafnium oxide phases in MIM configuration which is typical for RRAM devices. Special attention was given to the influence of surface oxidation effects. Here, it was found that the oxygen-deficient phases r-HfO₁.₇ and hcp-HfO₀.₇ exhibit high ohmic conductivity as expected, but stable bipolar switching behavior as a result of oxidation in air. Here, the mechanism of this behavior was discussed and the role of the r-HfO₁.₇ and hcp-HfO₀.₇ phases as novel electrode materials in hafnium oxide-based RRAM in particular. In collaboration with the electron microscopy group of Prof. Molina Luna, the studied phases, which have been characterized by rather macroscopic techniques so far, have been analyzed by wide-ranging TEM methodology. The strong oxygen deficiency in combination with the verified electrical conductivity of r-HfO₁.₇ and hcp-HfO₀.₇ shows the importance of the identification of these phases on the nanoscale. Such abilities are essential for the planned characterization of the "conducting-filament" mechanism. Here, the ability to distinguish m-HfO₂, r-HfO₁.₇, and hcp-HfO₀.₇ using high-resolution transmission electron microscopy (HRTEM), Automated Crystal Orientation and Phase Mapping (ACOM), and Electron Energy Loss Spectroscopy (EELS), is demonstrated and the necessity of combined measurements for reliable phase identification was discussed. Finally, a series of monoclinic to rhombohedral hafnium oxide was investigated in a cooperative study with FZ Jülich using scanning probe microscopy. Since recent studies in particular highlight the significance of the microstructure in stoichiometric hafnium oxide-based RRAM, the topological microstructure in the region of the phase transition to strongly oxygen deficient rhombohedral hafnium oxide was investigated. Special attention was given to the correlation of microstructure and conductivity. In particular, the influences of grain boundaries on electrical properties were discussed. In summary, this work provides comprehensive insights into the nature and properties of sub-stoichiometric hafnium oxide phases and their implications on the research of hafnium oxide-based RRAM technology. Taking into account a wide range of scientific perspectives, both, the validity of obtained results and the wide range of their application is demonstrated. Thus, this dissertation provides a detailed scientific base to the understanding of hafnium oxide-based electronics

    Low Power Memory/Memristor Devices and Systems

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    This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

    Ferrimagnetic rare-earth-transition-metal heterostructures: implications for future data storage, sensors, and unconventional computing

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    In this work, different ferrimagnetic rare-earth-transition-metal heterostructures are investigated. The findings provide implications for future data storage, sensor, and unconventional computing devices. In the first part, ferri- and ferromagnetic films are exchange-coupled and studied as potential composite media for magnetic recording technologies. For this, the underlying individual layers are examined, too. Within this study, the influence of Pd and Pt insertion layers in ferromagnetic Co/Ni multilayers is investigated. In these systems, the maximum effective magnetic anisotropy is more than doubled by the introduced insertion layers, while the initial saturation magnetization and Curie temperature are reduced. Further, amorphous Tb-FeCo alloys and multilayers are studied as the second building block of the desired composite medium. In particular, the structural and magnetic properties are analyzed upon post-annealing. At temperatures above 400 K, irreversible effects on the structural properties are found, which also influence the magnetic properties. It is shown that these changes in properties cannot be prevented by tuning the composition or by a multilayer structure of the film. However, key insights on the structural and magnetic properties upon annealing are provided for future high-temperature devices. Afterward, the exchange-coupled ferrimagnetic/ferromagnetic bilayer is studied. Measurements on the dependency on temperature, the ferrimagnetic composition, and the thickness of the ferromagnet are carried out. Two distinct magnetic reversal mechanisms are revealed. The reversal characteristics depend critically on the thickness of the ferromagnetic layer. The underlying microscopic origin is revealed by high-resolution magnetic force microscopy. Above a certain thickness of the ferromagnet, the switching process is driven by in-plane domain wall propagation. In contrast, thinner ferromagnetic layers exhibit a nucleation-dominated reversal due to grain-to-grain variations in magnetic anisotropy. Although the realization of an exchange-coupled composite medium for magnetic recording can not be achieved, insights for the future realization of sub micron high energy density permanent magnets and spintronic devices are gained. In the second part of this work, topologically protected spin structures, including skyrmions and antiskyrmions, are investigated in Fe/Gd-based multilayers. Particularly in coexisting phases, different topologically protected magnetic quasi-particles may show fascinating physics and potential for spintronic devices. While skyrmions are observed in a wide range of materials, until now, antiskyrmions have been exclusive to materials with D2d or S4 symmetry. In this work, first and second-order antiskyrmions are stabilized for the first time by magnetic dipole-dipole interaction. Using Lorentz transmission electron microscopy imaging, coexisting first- and second-order antiskyrmions, Bloch skyrmions, and type-2 bubbles are observed, and the range of material properties and magnetic fields where the different spin objects form and dissipate is determined. The discovered phase pocket of metastable antiskyrmions for low saturation magnetization and uniaxial magnetic anisotropy values is confirmed by micromagnetic simulations and represents a recipe, which has to be satisfied for the stabilization of antiskyrmions by dipole-dipole interaction in other material systems. Furthermore, the nucleation process of the spin objects and the influence of an in-plane magnetic field are studied. Additionally, post-deposition techniques are employed to locally change the anisotropy of the samples and influence the nucleation and stability range of the spin objects. The gained knowledge significantly simplifies future investigations of antiskyrmions. Moreover, the coexisting phases of different topologically protected spin objects and their controlled nucleation provide great potential for further studies on magnetic quasi-particle interactions, spin dynamics, as well as for possible future applications in spintronics, namely the racetrack memory, skyrmionic interconnections, skyrmion-based unconventional computing, and sensor devices

    Atomistic simulations of nanoscale molecular and metal oxide junctions

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    The push to continually improve computing power through the further miniaturisation of electronic devices has led to an explosion of "post-Moore" technologies such as molecular electronics and quantum computing. The downscaling of electronic devices has enhanced the importance of quantum effects. As a result to aid in the understanding and development of new devices, accurate and efficient atomistic material modelling methods are crucial for guiding experiments. In this thesis first principle material modelling (e.g Density Functional Theory) is combined with the atomistic Non Equilibrium Green’s Function quantum transport method to study how the electronic structure of two interesting junction systems relate to the electron transport through the junction. These two types of junctions, molecular and metal oxide, have crucial roles to play in the development of molecular based memories and superconducting quantum computing respectively. The first half of this thesis shows how the electronic structure of Polyoxometalate molecules dominate their electron transport properties whilst their redox ability makes them promising for memory applications. The results of the simulations reveal how the charge-balancing counterions of Polyoxometalates increase the conductance of the molecular junctions by stabilisation of unoccupied states, this is a key discovery as the effect of counterions are typically ignored. Polyoxometalates can be altered easily by changing the identity of the central caged atom, enhancing device engineering possibilities. The IV characteristics and capacitance are computed for Polyoxometalates with different caged atoms, the results show how the charge transport and storage can be engineered by choice of caged species and redox state. In the second half of this work, the archetypal Josephson junction, Al/AlOx/Al is explored. The goal was to understand from an atomistic point of view how the nature of the amorphous barrier influences the electron transport. The calculations provide evidence that the oxide concentration of the amorphous barrier significantly influences the resistance of the junction, it is found that oxygen deficient barriers lead to higher than expected critical currents. Unexpectedly the simulations here fail to show an exponential relationship between barrier length and resistance of the device. It is argued that there is an effective barrier length smaller than the physical barrier length due to thinner regions of the barrier. This highlights how important an understanding of the atomic structure of these junctions are for designing high quality junctions for superconducting qubits

    Robust, Energy-Efficient, and Scalable Indoor Localization with Ultra-Wideband Technology

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    Ultra-wideband (UWB) technology has been rediscovered in recent years for its potential to provide centimeter-level accuracy in GNSS-denied environments. The large-scale adoption of UWB chipsets in smartphones brings demanding needs on the energy-efficiency, robustness, scalability, and crossdevice compatibility of UWB localization systems. This thesis investigates, characterizes, and proposes several solutions for these pressing concerns. First, we investigate the impact of different UWB device architectures on the energy efficiency, accuracy, and cross-platform compatibility of UWB localization systems. The thesis provides the first comprehensive comparison between the two types of physical interfaces (PHYs) defined in the IEEE 802.15.4 standard: with low and high pulse repetition frequency (LRP and HRP, respectively). In the comparison, we focus not only on the ranging/localization accuracy but also on the energy efficiency of the PHYs. We found that the LRP PHY consumes between 6.4–100 times less energy than the HRP PHY in the evaluated devices. On the other hand, distance measurements acquired with the HRP devices had 1.23–2 times lower standard deviation than those acquired with the LRP devices. Therefore, the HRP PHY might be more suitable for applications with high-accuracy constraints than the LRP PHY. The impact of different UWB PHYs also extends to the application layer. We found that ranging or localization error-mitigation techniques are frequently trained and tested on only one device and would likely not generalize to different platforms. To this end, we identified four challenges in developing platform-independent error-mitigation techniques in UWB localization, which can guide future research in this direction. Besides the cross-platform compatibility, localization error-mitigation techniques raise another concern: most of them rely on extensive data sets for training and testing. Such data sets are difficult and expensive to collect and often representative only of the precise environment they were collected in. We propose a method to detect and mitigate non-line-of-sight (NLOS) measurements that does not require any manually-collected data sets. Instead, the proposed method automatically labels incoming distance measurements based on their distance residuals during the localization process. The proposed detection and mitigation method reduces, on average, the mean and standard deviation of localization errors by 2.2 and 5.8 times, respectively. UWB and Bluetooth Low Energy (BLE) are frequently integrated in localization solutions since they can provide complementary functionalities: BLE is more energy-efficient than UWB but it can provide location estimates with only meter-level accuracy. On the other hand, UWB can localize targets with centimeter-level accuracy albeit with higher energy consumption than BLE. In this thesis, we provide a comprehensive study of the sources of instabilities in received signal strength (RSS) measurements acquired with BLE devices. The study can be used as a starting point for future research into BLE-based ranging techniques, as well as a benchmark for hybrid UWB–BLE localization systems. Finally, we propose a flexible scheduling scheme for time-difference of arrival (TDOA) localization with UWB devices. Unlike in previous approaches, the reference anchor and the order of the responding anchors changes every time slot. The flexible anchor allocation makes the system more robust to NLOS propagation than traditional approaches. In the proposed setup, the user device is a passive listener which localizes itself using messages received from the anchors. Therefore, the system can scale with an unlimited number of devices and can preserve the location privacy of the user. The proposed method is implemented on custom hardware using a commercial UWB chipset. We evaluated the proposed method against the standard TDOA algorithm and range-based localization. In line of sight (LOS), the proposed TDOA method has a localization accuracy similar to the standard TDOA algorithm, down to a 95% localization error of 15.9 cm. In NLOS, the proposed TDOA method outperforms the classic TDOA method in all scenarios, with a reduction of up to 16.4 cm in the localization error.Cotutelle -yhteisväitöskirj

    Computational Intelligence for Cooperative Swarm Control

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    Over the last few decades, swarm intelligence (SI) has shown significant benefits in many practical applications. Real-world applications of swarm intelligence include disaster response and wildlife conservation. Swarm robots can collaborate to search for survivors, locate victims, and assess damage in hazardous environments during an earthquake or natural disaster. They can coordinate their movements and share data in real-time to increase their efficiency and effectiveness while guiding the survivors. In addition to tracking animal movements and behaviour, robots can guide animals to or away from specific areas. Sheep herding is a significant source of income in Australia that could be significantly enhanced if the human shepherd could be supported by single or multiple robots. Although the shepherding framework has become a popular SI mechanism, where a leading agent (sheepdog) controls a swarm of agents (sheep) to complete a task, controlling a swarm of agents is still not a trivial task, especially in the presence of some practical constraints. For example, most of the existing shepherding literature assumes that each swarm member has an unlimited sensing range to recognise all other members’ locations. However, this is not practical for physical systems. In addition, current approaches do not consider shepherding as a distributed system where an agent, namely a central unit, may observe the environment and commu- nicate with the shepherd to guide the swarm. However, this brings another hurdle when noisy communication channels between the central unit and the shepherd af- fect the success of the mission. Also, the literature lacks shepherding models that can cope with dynamic communication systems. Therefore, this thesis aims to design a multi-agent learning system for effective shepherding control systems in a partially observable environment under communication constraints. To achieve this goal, the thesis first introduces a new methodology to guide agents whose sensing range is limited. In this thesis, the sheep are modelled as an induced network to represent the sheep’s sensing range and propose a geometric method for finding a shepherd-impacted subset of sheep. The proposed swarm optimal herding point uses a particle swarm optimiser and a clustering mechanism to find the sheepdog’s near-optimal herding location while considering flock cohesion. Then, an improved version of the algorithm (named swarm optimal modified centroid push) is proposed to estimate the sheepdog’s intermediate waypoints to the herding point considering the sheep cohesion. The approaches outperform existing shepherding methods in reducing task time and increasing the success rate for herding. Next, to improve shepherding in noisy communication channels, this thesis pro- poses a collaborative learning-based method to enhance communication between the central unit and the herding agent. The proposed independent pre-training collab- orative learning technique decreases the transmission mean square error by half in 10% of the training time compared to existing approaches. The algorithm is then ex- tended so that the sheepdog can read the modulated herding points from the central unit. The results demonstrate the efficiency of the new technique in time-varying noisy channels. Finally, the central unit is modelled as a mobile agent to lower the time-varying noise caused by the sheepdog’s motion during the task. So, I propose a Q-learning- based incremental search to increase transmission success between the shepherd and the central unit. In addition, two unique reward functions are presented to ensure swarm guidance success with minimal energy consumption. The results demonstrate an increase in the success rate for shepherding

    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
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