Adaptive optics wavefront compensation for solid immersion microscopy in backside imaging

Thesis (Ph.D.)--Boston UniversityThis dissertation concerns advances in high-resolution optical microscopy needed to detect faults in next generation semiconductor chips. In this application, images are made through the chips' back side to avoid opaque interconnect metal layers on the frontside. Near infrared wavelengths are required, since the silicon is relatively transparent at these wavelengths. A significant challenge in this technique is to resolve features as small as 200nm using wavelengths exceeding 1OOOnm. The highest imaging resolution achievable with refractive optics at infrared wavelengths is demonstrated in this dissertation using an aplanatic solid immersion lens (SIL). This is the only method that has been found to be of sufficient resolution to image the next generation of integrated circuits. While the use of an aplanatic solid immersion lens theoretically allows numerical aperture far in excess of conventional microscopy (NASIL ~ 3.5), it also makes the system performance particularly sensitive to aberrations, especially when the samples have thicknesses that are more than a few micrometers thicker or thinner than designed thickness, or when the refractive index of the SIL is slightly different than that of the sample. In the work described here, practical design considerations of the SILs are examined. A SIL-based confocal scanning microscope system is designed and constructed. The aberrations of the system due to thickness uncertainty and material mismatch are simulated using both analytical model and ray-tracing software, and are measured in the SIL experimental apparatus. The dominant aberration for samples with thickness mismatch is found to be spherical aberration. Wavefront errors are compensated by a microelectromechanical systems deformable mirror (MEMS DM) in the optical system's pupil. The controller is implemented either with closed-loop real time sensor feedback or with predictive open-loop estimation of optical aberrations. Different DM control algorithms and aberration compensation techniques are studied and compared. The experimental results agree well with simulation and it has been demonstrated through models and experiments in this work that the stringent sample thickness tolerances previously needed for high numerical aperture SIL microcopy can be relaxed considerably through aberration compensation. Near-diffraction-limited imaging performance has been achieved in most cases that correspond to practical implementation of the technique

Recent Developments in Atomic Force Microscopy and Raman Spectroscopy for Materials Characterization

This book contains chapters that describe advanced atomic force microscopy (AFM) modes and Raman spectroscopy. It also provides an in-depth understanding of advanced AFM modes and Raman spectroscopy for characterizing various materials. This volume is a useful resource for a wide range of readers, including scientists, engineers, graduate students, postdoctoral fellows, and scientific professionals working in specialized fields such as AFM, photovoltaics, 2D materials, carbon nanotubes, nanomaterials, and Raman spectroscopy

単層カーボンナノチューブ/ポルフィリン-ポリ酸ランダムネットワークを用いたマテリアルリザバー演算素子 —次世代機械知能への新規アプローチ

In a layman’s term, computation is defined as the execution of a given instruction through a programmable algorithm. History has it that starting from the simplest calculator to the sophisticated von Neumann machine, the above definition has been followed without a flaw. Logical operations for which a human takes a minute long to solve, is a matter of fraction of seconds for these gadgets. But contrastingly, when it comes to critical and analytical thinking that requires learning through observation like the human brain, these powerful machines falter and lag behind. Thus, inspired from the brain’s neural circuit, software models of neural networks (NN) integrated with high-speed supercomputers were developed as an alternative tool to implement machine intelligent tasks of function optimization, pattern, and voice recognition. But as device downscaling and transistor performance approaches the constant regime of Moore’s law due to high CMOS fabrication cost and large tunneling energy loss, training these algorithms over multiple hidden layers is turning out to be a grave concern for future applications. As a result, the interplay between faster performance and low computational power requirement for complex tasks deems highly disproportional. Therefore, alternative in terms of both NN models and conventional Neumann architecture needs to be addressed in today’s age for next-generation machine intelligence systems. Fortunately, through extensive research and studies, unconventional computing using a reservoir based neural network platform, called in-materio reservoir computing (RC) has come to the rescue. In-maerio RC uses physical, biological, chemical, cellular automata and other inanimate dynamical systems as a source of non-linear high dimensional spatio-temporal information processing unit to construct a specific target task. RC not only has a three-layer simplified neural architectural layer, but also imposes a cheap, fast, and simplified optimization of only the readout weights with machine intelligent regression algorithm to construct the supervised objective target via a weighted linear combination of the readouts. Thus, utilizing this idea, herein in this work we report such an in-materio RC with a dynamical random network of single walled carbon nanotube/porphyrin-polyoxometalate (SWNT/Por-POM) device. We begin with Chapter 1, which deals with the introduction covering the literature of ANN evolution and the shortcomings of von Neumann architecture and training models of these ANN, which leads us to adopt the in-materio RC architecture. We design the problem statement focused on extending the theoretical RC model of previously suggested SWNT/POM network to an experimental one and present the objective of fabricating a random network based on nanomaterials as they closely resemble the network structure of the brain. Finally, we conclude by stating the scope of this research work aiming towards validating the non-linear high dimensional reservoir property SWNT/Por-POM holds for it to explicitly demonstrate the RC benchmark tasks of optimization and classification. Chapter 2 describes the methodology including the chemical repository required for the facile synthesis of the material. The synthesis part is divided broadly into SWNT purification and then its dispersion with Por-POM to form the desired complex. It is then followed up with the microelectrode array fabrication and the consequent wet-transfer thin film deposition to give the ultimate reservoir architecture of input-output control read pads with SWNT/Por-POM reservoir. Finally we give a briefing of AFM, UV-Vis spectroscopy, FE-SEM characterization techniques of SWNT/Por-POM complex along with the electrical set-up interfaced with software algorithm to demonstrate the RC approach of in-materio machine intelligence. In Chapter 3, we study the current dynamics as a function of voltage and time and validate the non-linear information processing ability intrinsic to the device. The study reveals that the negative differential resistance (NDR) arising from redox nature of Por-POM results in oscillating random noise outputs giving rise to 1/f brain-like spatio-temporal information. We compute the memory capacity (MC) and prove that the device exhibits echo state property of fading memory, but remembers very little of the past information. The low MC and high non-linearity allowed us to choose mostly non-linear tasks of waveform generation, Boolean logic optimization and one-hot vector binary object classification as the RC benchmark. The Chapter 4 relates to the waveform generation task. Utilizing the high dimensional voltage readouts of varying amplitude, phase and higher harmonic frequencies, relative to input sine wave, a regression optimization was performed towards constructing cosine, triangular, square and sawtooth waves resulting in a high accuracy of around 95%. The task complexity of function optimization was further enhanced in Chapter 5 where two inputs were used to construct Boolean logic functions of OR, AND, XOR, NOR, NAND and XNOR. Similar to the waveform, accuracy over 95% could be achieved due to the presence of NDR nonlinearity. Furthermore, the device was also tested for classification problem in Chapter 6. Here we showed an off-line binary classification of four object toys; hedgehog, dog, block and bus, using the grasped tactile information of these objects as inputs obtained from the Toyota Human Support Robot. A one-ridge regression analysis to fit the hot vector supervised target was used to optimize the output weights for predicting the correct outcome. All the objects were successfully classified owing to the 1/f information processing factor. Lastly, we conclude the section in Chapter 7 with the future scope of extending the idea to fabricate a 3-D model of the same material as it opens up opportunity for higher memory capacity fruitful for future benchmark tasks of time-series prediction. Overall, our research marks a step stone in utilizing SWNT/Por-POM as the in-materio RC for the very first time thereby making it a desirable candidate for next-generation machine intelligence.九州工業大学博士学位論文 学位記番号：生工博甲第425号 学位授与年月日：令和3年12月27日1 Introduction and Literature review|2 Methodology|3 Reservoir dynamics emerging from an incidental structure of single-walled carbon nanotube/porphyrin-polyoxometalate complex|4 Fourier transform waveforms via in-materio reservoir computing from single-walled carbon nanotube/porphyrin-polyoxometalate complex|5 Room temperature demonstration of in-materio reservoir computing for optimizing Boolean function with single-walled carbon nanotube/porphyrin-polyoxometalate composite|6 Binary object classification with tactile sensory input information of via single-walled carbon nanotube/porphyrin-polyoxometalate network as in-materio reservoir computing|7 Future scope and Conclusion九州工業大学令和3年

Precision measurement of microscopic images

The problems of measuring the dimensions of small geometries using an optical microscope are investigated, with particular attention to the measurement of the critical linewidths on semiconductor integrated circuit wafers and photomasks. Conventional scalar diffraction models are used to investigate the imaging process of the optical microscope and these are extended to include image transducers and measurement devices. Particular attention is paid to the use of a video camera as an image transducer and an image shearing module as a measurement device. The performance of different measurement techniques is investigated both theoretically and experimentally and systematic errors in different measurement techniques are identified. The limits of scalar diffraction theory are investigated experimentally. The design of digital array filters is investigated in order to develop algorithms for automating the location of the feature to be measured, focusing and the measurement process itself. A novel method of automating the image shearing measurement technique using array filters is presented. The models for the optical imaging, transducer response and automation algorithms are used to develop an automated image shearing based measurement system for measuring the gaps in magnetic recording heads. The design of the system is described and experimental performance tests demonstrate good agreement with the predictions of the theoretical system models. The problem of modelling the images formed by thick layer objects is considered and a waveguide model is developed. Experimental and theoretical tests of the model show that the image profiles of shaped, multi-layer objects can be successfully predicted. The model is used to investigate the imaging of thick layer objects in order to study the performance of different linewidth measurement techniques. A novel method of improving the repeatability of measurements on thick layers is presented, based correction technique

Optical investigation of many-body interactions in transition metal dichalcogenide heterostructures

Two-dimensional transition metal dichalcogenide (TMD) heterostructures have emerged as a novel platform for the investigation of many-body physical phenomena. In these systems, tightly bound excitons dressed by a gate-tunable Fermi sea form exciton-polarons, which are sensitive to Coulomb and spin interactions. In addition, TMD moir´e devices provide a highly tunable platform to study strongly correlated electronic states. This thesis describes the use of magneto-optical polarisation-resolved white-light confocal reflection spectroscopy at cryogenic temperatures (4 K) to probe different many-body interactions in TMD heterostructure devices. First, monolayer and bilayer tungsten diselenide (WSe2) and molybdenum diselenide (MoSe2) are investigated under varying carrier concentration. The doping dependent dispersions of the exciton-polarons are shown to be excellent probes of the distinctive band structures of these materials. Then, in a moir´e heterobilayer MoSe2/WSe2 structure, optically injected excitons are shown to interact with itinerant carriers occupying narrow moir´e bands to form exciton-polarons sensitive to strong correlations. At a multitude of fractional fillings of the moir´e lattice, the ordering of both electrons and holes into stable correlated electronic states is observed, leading to extraordinary Zeeman splittings of the exciton-polarons. Next, in heterotrilayer bilayer WSe2/monolayer MoSe2, the energetic ordering of the moir´e bands is shown to be highly tunable with applied vertical electric field, leading to the demonstration of hole transfer between correlated states in K and Γ valley derived moir´e bands. Finally, the moire lattice uniformity of MoSe2/WSe2 moir´e heterostructures is probed by spatial mapping of the electronic correlations, leading to a measured variation in twist angle of 0.6 degrees across the device. These results establish WSe2 and MoSe2 heterostructures as an exciting platform for investigations of exciton-polarons, Fermi-Hubbard or Bose-Hubbard physics

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

Electromigration in thin films of aluminium

Imperial Users onl

Corrosion and Degradation of Materials

Studies on the corrosion and degradation of materials play a decisive role in the novel design and development of corrosion-resistant materials, the selection of materials used in harsh environments in designed lifespans, the invention of corrosion control methods and procedures (e.g., coatings, inhibitors), and the safety assessment and prediction of materials (i.e., modelling). These studies cover a wide range of research fields, including the calculation of thermodynamics, the characterization of microstructures, the investigation of mechanical and corrosion properties, the creation of corrosion coatings or inhibitors, and the establishment of corrosion modelling. This Special Issue is devoted to these types of studies, which facilitate the understanding of the corrosion fundamentals of materials in service, the development of corrosion coatings or methods, improving their durability, and eventually decreasing corrosion loss

Glaces kagomé de spins artificiels : de la dégénérescence à courte-portée vers l'ordre dipolaire

Artificial spin networks were initially proposed as toy-spin models destined for the investigation of magnetic frustration effects in two-dimensional spin lattices, a complementary approach to the study of the magnetic frustration encountered in spin ice pyrochlores. Generally fabricated via lithography techniques, these arrays of nano-scale magnetic islands can be designed at-will. Given the size and shape of the elements, their magnetization is almost uniform throughout their volume, thus making these islands act like classical Ising spins. Combined with the possibility of individually imaging the magnetic degrees of freedom in real space, these systems offer an almost infinite playground for the investigation of competing interactions in magnetostatic frameworks and potential for the experimental discovery of novel and exotic magnetic phases. However, unlike their condensed matter counterparts, first-generation artificial spin networks are insensitive to thermal fluctuations, requiring other driving mechanisms for accessing their complex low-energy manifolds. A field-protocol has been employed for driving such networks towards their ground-state configurations, although they only partially manage to accommodate pair-island interactions. More recently, thermally-active artificial spin networks have been introduced, surpassing the limits of demagnetized arrays in the quest for exotic low-energy spin textures.This thesis presents experimental and numerical studies performed on artificial kagome spin arrays, one of the most frustrated two-dimensional lattices. The kagome spin ice geometry has received most of the community's attention as it presents highly degenerate manifolds and unconventional spin textures. Within a dipolar long-range framework, it displays a low-temperature regime characterized by the coexistence of a crystalline phase, associated to the magnetic charge, and a disordered spin lattice. While demagnetizing such artificial kagome arrays cannot access this exotic state, thermally-active networks can locally retrieve such a phase, creating crystallites of antiferromagnetically-ordered magnetic charges. The first part of this work presents the experimental protocol employed to this purpose. A kinetic model is also proposed that successfully captures the observed experimental features and explains the efficiency of this approach.The second part of the current thesis presents a study of a novel artificial spin ice system, the artificial kagome Ising network. This network primarily differs from the kagome spin ice array by having its magnetic moments pointing along the vertical axis. A recent study of this system has concluded that, after demagnetization, these two artificial kagome networks display similar pairwise spin correlation development and their final frozen states can be well characterized by short-range interaction models. Through the use of demagnetization protocols, magnetic force microscopy and Monte Carlo simulations, it is demonstrated that long-range dipolar interactions between the magnetic elements cannot be neglected when describing the remanent states of demagnetized artificial kagome Ising networks. These results assess the limits of the reported universal behavior of artificial kagome lattices and enrich the spectrum of magnetic phases that could be achieved with such nanostructured systems. Indeed, Monte Carlo simulations indicate that this kagome Ising network presents a different low-energy behavior than kagome spin ice, the incipient stages of which have been accessed experimentally, but its dipolar ground-state configuration remains unknown. Nevertheless, by inspecting the low-temperature thermodynamic features of this array and through the use of a geometrical construction, a ground-state candidate is provided.Les réseaux artificiels de spin ont été initialement introduites pour l'étude des effets de frustration géométrique dans des réseaux bidimensionnelles de spin, un approche complémentaire à l'étude de la frustration rencontré dans les glaces pyrochlores de spin. Généralement fabriqués en utilisant des techniques de lithographie, ces réseaux de nanoaimants peuvent être élaborer avec une grande degré de liberté. Etant donné la taille et la forme de ces plots magnétiques, l'aimantation est presque uniforme dans tout leur volume, un aspect qui fait que ces aimants peuvent être considérés comme des spin Ising classique géants. Avec la possibilité d'imager chacun degrée de liberté magnétique dans l'espace direct, ces systèmes offrent un large spectre d'opportunités pour l'étude de la frustration dans un cadre magnétostatiques bidimensionnelle et la potentielle découverte de phases magnétiques exotiques. Toutefois, contrairement à leurs homologues de la matière condensée, la première génération de glaces de spin artificiels sont pratiquement insensibles aux fluctuations thermiques. Par conséquence, d'autres dynamiques sont nécessaires pour amener ces systèmes vers leurs variétés de basse énergie et un protocole de désaimantation a été généralement utilisé dans ce sens, mais ce processus arrivent à accommoder juste partiellement les interactions entre les nanoaimants. Plus récemment, des réseaux artificiels de spin thermiquement-actives ont été introduits, permettant de dépasser les limitations des réseaux désaimantes pour la recherche des textures de spin exotiques.Cette thèse présente des études expérimentales et numériques réalisés sur des réseaux kagomé de spin. La glace artificielle kagomé planaire a été un point central d'intérêt pendant les dernières années, grâce à ses variétés énergétiques hautement dégénérés et aux textures de spin non-conventionnelles. Ainsi, dans un cadre magnétostatique, il présent une phase exotique caractérisée par la coexistence d'un état cristallin, associée à la charge magnétique, et un réseau de spin désordonnés. Bien que la désaimantation n'arrive pas d'accéder cet état remarquable, les réseaux thermiquement actives ont réussi de créer des cristallites de cette phase. La première partie de ce travail présente le protocole expérimental utilisé pour réaliser cet état. En plus, un modèle cinétique est proposé qui reproduit avec succès les caractéristiques observées et explique l'efficacité de cette approche.Dans un deuxième temps, un étude sur un nouveau système de glace de spin artificielle est présenté: le réseau kagomé Ising artificielle. Ce système présentent des moments magnétiques qui pointent selon l'axe verticale, contrairement au réseau kagomé planaire. Un étude récent sur ce système a conclu que, après la démagnétisation, ces deux réseaux kagomé artificiels présentent des corrélations de spins similaires et leurs états magnétiques rémanentes peuvent être bien caractérisées par des modèles de spin basés sur des interactions à courte portée. Avec des protocoles de désaimantation, des mesures de microscopie à force magnétique et des simulations Monte Carlo, il est montré que les interactions dipolaires à longue portée entre les éléments magnétiques ne peuvent pas être négligés lors de la description des états rémanents des réseaux kagome Ising artificiels désaimantées. Ces résultats limitent la validité du comportement universel entre les deux réseaux kagomé artificiels et enrichissent la palette de phases magnétiques qui peuvent être réaliser avec de tels systèmes nanostructurés. Les simulations Monte Carlo indiquent que ce réseau kagomé Ising présente un comportement de basse énergie différente de la glace kagomé planaire, mais la variétés fondamentale dans ce cadre dipolaire reste inconnu. Toutefois, en inspectant ses caractéristiques thermodynamiques à basse température et grâce une construction géométrique, un candidat pour l'état fondamental est fourni

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise