Experiments on synthetic dimensions in photonics

The first and introductory section of the dissertation presents the working principle of a one- and two-dimensional photonic mesh lattice based on the time-multiplexing technique. The basis of a random walk interrelated to the corresponding light and quantum walk is comprehensively discussed as well. The second part of the dissertation consists of three experiments on a one-dimensional photonic mesh lattice. Firstly, the Kapitza-based guiding light project models the Kapitza potential as a continuous Pauli-Schrödinger-like equation and presents an experimental observation of light localization when the transverse modulation is bell-shaped but with a vanishing average along the propagation direction. Secondly, the optical thermodynamics project experimentally demonstrates for the first time that any given initial modal occupancy reaches thermal equilibrium by following a Rayleigh-Jeans distribution when propagates through a multimodal photonic mesh lattice with weak nonlinearity. Remarkably, the final modal occupancy possesses a unique temperature and chemical potential that have nothing to do with the actual thermal environment. Finally, the quantum interference project discusses an experimental all-optical architecture based on a coupled-fiber loop for generating and processing time-bin entangled single-photon pairs. Besides, it shows coincidence-to-accidental ratio and quantum interference measurements relying on the phase modulation of those time bins. The third part of the dissertation comprises two experiments on a two-dimensional photonic mesh lattice. The first project discusses the experimental realization of a two-dimensional mesh lattice employing short- and long-range interaction. To some extent, the second project presents a nonconservative system based on a two-dimensional photonic mesh lattice exploiting parity-time (PT) symmetry

Tailored electrical characteristics in multilayer metal-oxide-based-memristive devices

Auf Mehrlagen-Metalloxiden basierende memristive Bauelemente sind einer der vielversprechendsten Kandidaten für neuromorphes Computing. Allerdings stellen spezifische Anwendungen des neuromorphen Computings unterschiedliche Anforderungen an die memristiven Bauelemente. Eine ungelöste Herausforderung in der technologischen Entwicklung ist daher das maßgeschneiderte Design von memristiven Bauelementen für spezifische Anwendungen. Insbesondere die unterschiedlichen Materialien des Schichtstapels erschweren die Herstellungsprozesse aufgrund einer großen Anzahl von Parametern, wie z. B. der Stapelsequenzen und -dicken und der Qualität sowie der Eigenschaften der einzelnen Schichten. Daher sind systematische Untersuchungen der einzelnen Bauelementparameter besonders entscheidend. Darüber hinaus müssen sie mit einem tiefgreifenden Verständnis der zugrundeliegenden physikalischen Prozesse kombiniert werden, um die Lücke zwischen Materialdesign und elektrischen Eigenschaften der resultierenden memristiven Bauelemente zuschließen. Um memristive Bauelemente mit unterschiedlichen resistiven Schalteigenschaften zu erhalten, werden verschiedene Abfolgen und Kombinationen von drei Metalloxidschichten (TiOx, HfOx, und AlOx) hergestellt und untersucht. Zunächst werden einschichtige Oxidbauelemente untersucht, um Kandidaten für mehrschichtige Stapel zu identifizieren. Zweitens werden zweischichtige TiOx/HfOx Oxidbauelemente hergestellt. Anhand von systematischen Experimenten und statistischen Analysen wird gezeigt, dass die Stöchiometrie, die Dicke, und die Fläche des Bauelements die Betriebsspannungen, die Nichtlinearität beim resistiven Schalten und die Variabilität beeinflussen. Drittens werden TiOx/AlOx/HfOx-basierte Bauelemente hergestellt. Durch das Hinzufügen von AlOx in die zweischichtigen Oxidstapel weisen diese dreischichtigen Bauelemente optimale elektrische Eigenschaften für den Einsatz in neuromorpher Hardware auf, wie z. B. elektroformierungsfreies und strombegrenzungsloses Schalten sowie eine lange Lebensdauer. Die entwickelten memristiven Bauelemente werden in Systeme, wie Kreuzpunkt-Strukturen und Ein-Transistor-ein-Memristor-Konfigurationen integriert. Hier wird die Eignung für effizientes neuromorphes Computing bewertet. Außerdem werden Methoden zur stufenlosen analogen Einstellung des Widerstands der Bauelemente demonstriert. Diese Eigenschaft ermöglicht effiziente neuromorphe Rechenschemata. Diese umfassende Studie beleuchtet die Beziehung zwischen den Bauelementparametern und den elektrischen Eigenschaften von mehrschichtigen memristiven Bauelementen auf Metalloxidbasis. Auf dieser Grundlage werden maßgeschneiderte Methoden für spezifische neuromorphe Anwendungen entwickelt.Multilayer metal-oxide-based-memristive devices are one of the most promising candidates for neuromorphic computing. However, specific applications of neuromorphic computing call for different requirements for memristive devices. Therefore, an open challenge in technological development is the tailored design of memristive devices for specific applications. In particular, multilayer stacks complicate fabrication processes due to a large number of device parameters such as staking sequences and thicknesses, quality, and property of each layer. Therefore, systematic investigations of the individual device parameters are particularly decisive. Moreover, they need to be combined with a profound understanding of the underlying physical processes to bridge the gap between material design and electrical characteristics of the resulting memristive devices. To obtain memristive devices with different resistance switching characteristics, various sequences and combinations of three metal oxide layers (TiOx, HfOx, and AlOx) are fabricated and studied. First, single-layer oxide devices are investigated to find desirable multilayer stacks for memristive devices. Second, TiOx/HfOx-based bilayer oxide devices are fabricated. Via systematic experiments and statistical analysis, it is shown that the stoichiometry, thickness, and device area influence operating voltages, non-linearity in resistive switching, and variability. Third, TiOx/AlOx/HfOx-based devices are fabricated. By adding AlOx into the bilayer oxide stacks, these trilayer devices present favorable electrical features for use in neuromorphic hardware, such as electroforming-free and compliance-free switching as well as long retention. The developed memristive devices are integrated into systems such as crossbar structures and one-transistor-one-memristor configurations. Here, suitability for efficient neuromorphic computing is assessed. Also, methods to tune the device resistance gradually in an analog fashion are demonstrated. This feature allows for efficient neuromorphic computation. This comprehensive study highlights the relationship between device parameters and electrical properties of multilayer metal-oxide-based memristive devices. On this basis, tailoring methodologies are established for specific neuromorphic applications

Field resolving spectrometer for mid-infrared molecular spectroscopy

The interrogation of molecular samples with broadband mid-infrared (MIR) radiation results in highly specific “vibrational fingerprints,” containing a wealth of information on molecular structure and composition. This renders vibrational spectroscopy a powerful and versatile tool for applications ranging from fundamental science to the life sciences and to industrial applications. Conventional MIR spectroscopic techniques face severe limitations in detection sensitivity, in particular due to the poor coherence properties of common MIR sources as well as to the moderate detectivity and dynamic range of broadband MIR detectors. The research reported in this thesis has addressed the quest for novel routes towards tapping the potential of MIR spectral fingerprinting, harnessing modern, high-power femtosecond laser technology. The first part of the work reports the construction of octave-spanning, coherent femtosecond MIR sources, employing state-of-the-art 100-W-average-power-level thin-disk Yb:YAG modelocked oscillators. We demonstrated ultrabroadband coherent MIR sources with a brilliance exceeding that of MIR beamlines at 3rd-generation synchrotrons, and found that pulses emerging via intra-pulse difference frequency generation offer superior (and unparalleled) optical-waveform stability as compared to standard optical-parametric amplification. The temporal confinement of broadband MIR radiation to trains of sub-100-femtosecond pulses, together with field-resolved detection via electro-optic sampling (EOS) affords detection of the molecular fingerprint signal in the near-infrared region, where highly-efficient, high-dynamic-range detectors exist. Optimized EOS detection enabled a linear response over an intensity dynamic range of 150 dB at a central wavelength of 8.6 µm. This exceeds the previous state of the art by a large margin and has paved the way to high-signal-to-noise-ratio transmission measurements of aqueous biological samples like living cells and tissue. The waveform stability of the mid-infrared pulses plays a crucial role for real-life field-resolved spectroscopy measurements, and is of paramount importance for precision-metrological applications. In the second part of this thesis, high-quantum-efficiency EOS was employed for precision measurements of waveform jitter, evaluated for millions of pulses. This study demonstrated few-attosecond temporal jitter in the 1-Hz-to-0.625-MHz band, between the centre of mass of the driving near-infrared pulses, and individual field zero-crossings of the emerging, broadband mid-infrared field. This confirms the outstanding waveform stability achievable with second-order parametric processes with an order-of-magnitude improved accuracy compared to previous measurements. Furthermore, chirping the MIR pulse revealed attosecond-level optical-frequency-dependent waveform jitter, whose dynamics were quantitatively traced back to excessive intensity noise of the mode-locked oscillator. Thus, this study validated EOS as a broadband (both in the radio-frequency and in the optical domain), high-sensitivity measurement technique for the dynamics of optical waveforms beyond the standard, optical-spectrum-integrating carrier-envelope phase model. The instrument developed during this thesis was utilized for the first highly sensitive field-resolved measurements in the MIR molecular fingerprint region. It enabled the detection of molecular concentrations spanning 5 orders of magnitude down to 200-ng/mL in aqueous solutions and the examination of living biological systems with a thickness of up to 0.2 mm. Currently, the instrument is being used for the first large-scale studies on disease recognition based on vibrational fingerprinting of human blood serum. The implementation of intra-scan referencing, successfully carried out in the last weeks of this doctoral work, together with fast-scanning techniques and the extension of the MIR spectral bandwidth which are underway at our laboratory, promise to extend the technology pioneered in this thesis to new levels of sensitivity and reproducibility in vibrational spectroscopy. In addition to directly benefitting analytical applications, these developments are likely to afford novel insights into light-matter interactions

LOW-POWER FREQUENCY SYNTHESIS BASED ON INJECTION LOCKING

Ph.DDOCTOR OF PHILOSOPH

Ultra Low Power Circuits for Internet of Things and Deep Learning Accelerator Design with In-Memory Computing

Collecting data from environment and converting gathered data into information is the key idea of Internet of Things (IoT). Miniaturized sensing devices enable the idea for many applications including health monitoring, industrial sensing, and so on. Sensing devices typically have small form factor and thus, low battery capacity, but at the same time, require long life time for continuous monitoring and least frequent battery replacement. This thesis introduces three analog circuit design techniques featuring ultra-low power consumption for such requirements: (1) An ultra-low power resistor-less current reference circuit, (2) A 110nW resistive frequency locked on-chip oscillator as a timing reference, (3) A resonant current-mode wireless power receiver and battery charger for implantable systems. Raw data can be efficiently transformed into useful information using deep learning. However deep learning requires tremendous amount of computation by its nature, and thus, an energy efficient deep learning hardware is highly demanded to fully utilize this algorithm in various applications. This thesis also presents a pulse-width based computation concept which utilizes in-memory computing of SRAM.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144173/1/myungjun_1.pd