Advanced CMOS Integrated Circuit Design and Application

The recent development of various application systems and platforms, such as 5G, B5G, 6G, and IoT, is based on the advancement of CMOS integrated circuit (IC) technology that enables them to implement high-performance chipsets. In addition to development in the traditional fields of analog and digital integrated circuits, the development of CMOS IC design and application in high-power and high-frequency operations, which was previously thought to be possible only with compound semiconductor technology, is a core technology that drives rapid industrial development. This book aims to highlight advances in all aspects of CMOS integrated circuit design and applications without discriminating between different operating frequencies, output powers, and the analog/digital domains. Specific topics in the book include: Next-generation CMOS circuit design and application; CMOS RF/microwave/millimeter-wave/terahertz-wave integrated circuits and systems; CMOS integrated circuits specially used for wireless or wired systems and applications such as converters, sensors, interfaces, frequency synthesizers/generators/rectifiers, and so on; Algorithm and signal-processing methods to improve the performance of CMOS circuits and systems

Temporal Data Analysis Using Reservoir Computing and Dynamic Memristors

Temporal data analysis including classification and forecasting is essential in a range of fields from finance to engineering. While static data are largely independent of each other, temporal data have a considerable correlation between the samples, which is important for temporal data analysis. Neural networks thus offer a more general and flexible approach since they do not depend on parameters of specific tasks but are driven only by the data. In particular, recurrent neural networks have gathered much attention since the temporal information captured by the recurrent connections improves the prediction performance. Recently, reservoir computing (RC), which evolves from recurrent neural networks, has been extensively studied for temporal data analysis as it can offer efficient temporal processing of recurrent neural networks with a low training cost. This dissertation presents a hardware implementation of the RC system using an emerging device - memristor, followed by a theoretical study on hierarchical architectures of the RC system. A RC hardware system based on dynamic tungsten oxide (WOx) memristors is first demonstrated. The internal short-term memory effects of the WOx memristors allow the memristor-based reservoir to nonlinearly map temporal inputs into reservoir states, where the projected features can be readily processed by a simple linear readout function. We use the system to experimentally demonstrate two standard benchmarking tasks: isolated spoken digit recognition with partial inputs and chaotic system forecasting. High classification accuracy of 99.2% is obtained for spoken digit recognition and autonomous chaotic time series forecasting has been demonstrated over the long term. We then investigate the influence of the hierarchical reservoir structure on the properties of the reservoir and the performance of the RC system. Analogous to deep neural networks, stacking sub-reservoirs in series is an efficient way to enhance the nonlinearity of data transformation to high-dimensional space and expand the diversity of temporal information captured by the reservoir. These deep reservoir systems offer better performance when compared to simply increasing the size of the reservoir or the number of sub-reservoirs. Low-frequency components are mainly captured by the sub-reservoirs in the later stages of the deep reservoir structure, similar to observations that more abstract information can be extracted by layers in the late stage of deep neural networks. When the total size of the reservoir is fixed, the tradeoff between the number of sub-reservoirs and the size of each sub-reservoir needs to be carefully considered, due to the degraded ability of the individual sub-reservoirs at small sizes. Improved performance of the deep reservoir structure alleviates the difficulty of implementing the RC system on hardware systems. Beyond temporal data classification and prediction, one of the interesting applications of temporal data analysis is inferring the neural connectivity patterns from the high-dimensional neural activity recording data. By computing the temporal correlation between the neural spikes, connections between the neurons can be inferred using statistics-based techniques, but it becomes increasingly computationally expensive for large scale neural systems. We propose a second-order memristor-based hardware system using the natively implemented spike-timing-dependent plasticity learning rule for neural connectivity inference. By incorporating biological features such as transmission delay to the neural networks, the proposed concept not only correctly infers the direct connections but also distinguishes direct connections from indirect connections. Effects of additional biophysical properties not considered in the simulation and challenges of experimental memristor implementation will be also discussed.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167995/1/moonjohn_1.pd

Physical reservoir computing with dynamical electronics

Since the advent of data-driven society, mass information generated from human activity and the natural environment has been collected, stored, processed, and then dispersed under conventional von Neumann architecture. However, further scaling the computing capability in terms of speed and power efficiency has been significantly slowed down in recent years due to the fundamental limits of transistors. To meet the increasingly demanding requirement for data-intensive computation, neuromorphic computing is a promising field taking the inspiration from the human brain, an extremely efficient biological computer, to develop unconventional computing paradigms for artificial intelligence. Reservoir computing, a recurrent neural network algorithm invented two decades ago, has received wide attention in the field of neuromorphic computing because of its unique recurrent dynamics and hardware-friendly implementation schemes. Under the concept of reservoir computing, hardware’s intrinsic physical behaviours can be explored as computing resources to keep the machine learning within the physical domain to improve processing efficiency, which is also known as physical reservoir computing. This thesis focuses on modelling and implementing physical reservoir computing based on dynamical electronics, along with its applications with sensory signals. First, the fundamental of the reservoir computing algorithm is introduced. Second, based on the reservoir algorithm and its functionalities, two different architectures for physically implementing reservoir computing, delay-based reservoir and parallel devices, are investigated to perform temporal signal processing. Thirdly, an efficient implementation architecture, namely rotating neurons reservoir, is developed. This novel architecture is evaluated in both theoretical analysis and experiments. An electrical prototype of the rotating neurons reservoir exhibits unique advantages such as resource-efficient implementation and low power consumption. More importantly, the theory of rotating neurons reservoir is highly universal, indicating that a rotational object embedded with dynamical elements can act as a reservoir computer

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Silver Filament Formation/Dissolution Dynamics Through a Polymer/Ionic liquid Composite by Direct-write

A direct-write, electrochemical approach to the formation and dissolution of silver nanofilaments is demonstrated through a novel polymer electrolyte consisting of a UV-crosslinkable polymer, polyethylene glycol diacrylate (PEGDA) and an ionic liquid (IL), 1-butyl-3-methylimadozolium hexafluorophosphate ([BMIM]PF6). Nanofilaments are formed and dissolved at pre-programmed locations with a conductive atomic force microscope (c-AFM) using a custom script. Although the formation time generally decreases with increasing bias from 0.7 to 3.0 V, an unexpected non-monotonic maximum is observed ~2.0 V. At voltages approaching this region of inverted kinetics, IL electric double layers (EDLs) become detectable; thus, the increased nanofilament formation time can be attributed to electric field screening, which hinders silver electromigration and deposition. Scanning electron microscopy confirms that nanofilaments formed in this inverted region have significantly more lateral and diffuse features. Time dependent formation currents reveal two types of nanofilament growth dynamics: abrupt, where the resistance decreases sharply over as little as a few ms, and gradual where it decreases more slowly over hundreds of ms. Whether the resistance change is abrupt or gradual depends on the extent to which the EDL screens the electric field. Silver nanofilaments with gradual growth dynamics have potential application in neuromorphic computing. In this study, a linear (R2 > 0.9) dependence of conductance on the number of bias pulses is demonstrated—a signature feature that is required for neuromorphic application. Hundreds of distinguishable conductance states ranging from 235 to 260 microsimens can be accessed using a low read bias. These results show that novel PEGDA/IL composite electrolyte enables the gradual formation and dissolution of silver nanofilament with tunable conductance states, making it a promising candidate to advance neuromorphic applications

Non-Standard Imaging Techniques

The first objective of the thesis is to investigate the problem of reconstructing a small-scale object (a few millimeters or smaller) in 3D. In Chapter 3, we show how this problem can be solved effectively by a new multifocus multiview 3D reconstruction procedure which includes a new Fixed-Lens multifocus image capture and a calibrated image registration technique using analytic homography transformation. The experimental results using the real and synthetic images demonstrate the effectiveness of the proposed solutions by showing that both the fixed-lens image capture and multifocus stacking with calibrated image alignment significantly reduce the errors in the camera poses and produce more complete 3D reconstructed models as compared with those by the conventional moving lens image capture and multifocus stacking. The second objective of the thesis is modelling the dual-pixel (DP) camera. In Chapter 4, to understand the potential of the DP sensor for computer vision applications, we study the formation of the DP pair which links the blur and the depth information. A mathematical DP model is proposed which can benefit depth estimation by the blur. These explorations motivate us to propose an end-to-end DDDNet (DP-based Depth and Deblur Network) to jointly estimate the depth and restore the image . Moreover, we define a reblur loss, which reflects the relationship of the DP image formation process with depth information, to regularize our depth estimate in training. To meet the requirement of a large amount of data for learning, we propose the first DP image simulator which allows us to create datasets with DP pairs from any existing RGBD dataset. As a side contribution, we collect a real dataset for further research. Extensive experimental evaluation on both synthetic and real datasets shows that our approach achieves competitive performance compared to state-of-the-art approaches. Another (third) objective of this thesis is to tackle the multifocus image fusion problem, particularly for long multifocus image sequences. Multifocus image stacking/fusion produces an in-focus image of a scene from a number of partially focused images of that scene in order to extend the depth of field. One of the limitations of the current state of the art multifocus fusion methods is not considering image registration/alignment before fusion. Consequently, fusing unregistered multifocus images produces an in-focus image containing misalignment artefacts. In Chapter 5, we propose image registration by projective transformation before fusion to remove the misalignment artefacts. We also propose a method based on 3D deconvolution to retrieve the in-focus image by formulating the multifocus image fusion problem as a 3D deconvolution problem. The proposed method achieves superior performance compared to the state of the art methods. It is also shown that, the proposed projective transformation for image registration can improve the quality of the fused images. Moreover, we implement a multifocus simulator to generate synthetic multifocus data from any RGB-D dataset. The fourth objective of this thesis is to explore new ways to detect the polarization state of light. To achieve the objective, in Chapter 6, we investigate a new optical filter namely optical rotation filter for detecting the polarization state with a fewer number of images. The proposed method can estimate polarization state using two images, one with the filter and another without. The accuracy of estimating the polarization parameters using the proposed method is almost similar to that of the existing state of the art method. In addition, the feasibility of detecting the polarization state using only one RGB image captured with the optical rotation filter is also demonstrated by estimating the image without the filter from the image with the filter using a generative adversarial network

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma