Aperiodic Multilayer Graphene Based Tunable and Switchable Thermal Emitter at Mid-infrared Frequencies

Over the past few decades, there have been tremendous innovations in electronics and photonics. The development of these ultra-fast growing technologies mostly relies on fundamental understanding of novel materials with unique properties as well as new designs of device architectures with more diverse and better functionalities. In this regard, the promising approach for next-generation nanoscale electronics and photonics is to exploit the extraordinary characteristics of novel nanomaterials. There has been an explosion of interest in graphene for photonic applications as it provides a degree of freedom to manipulate electromagnetic waves. In this thesis, to tailor the broadband blackbody radiation, new aperiodic multilayer structures composed of multiple layers of graphene and hexagonal boron nitride (hBN) are proposed as selective, tunable and switchable thermal emitters. To obtain the layer thicknesses of these aperiodic multilayer structures for maximum emittance/absorptance, a hybrid optimization algorithm coupled to a transfer matrix code is employed. The device simulation indicates that perfect absorption efficiency of unity can be achieved at very narrow frequency bands in the infrared under normal incidence. It has been shown that the chemical potential in graphene enables a promising way to design electrically controllable absorption/emission, resulting in selective, tunable and switchable thermal emitters at infrared frequencies. By simulating different aperiodic thermal emitters with different numbers of graphene layers, the effect of the number of graphene layers on selectivity, tunability, and switchability of thermal emittance is investigated. This study may contribute towards the realization of wavelength selective detectors with switchable intensity for sensing applications

Passive Laser Power Stabilization via an Optical Spring

Metrology experiments can be limited by the noise produced by the laser involved via small fluctuations in the laser's power or frequency. Typically, active power stabilization schemes consisting of an in-loop sensor and a feedback control loop are employed. Those schemes are fundamentally limited by shot noise coupling at the in-loop sensor. In this letter we propose to use the optical spring effect to passively stabilize the classical power fluctuations of a laser beam. In a proof of principle experiment, we show that the relative power noise of the laser is stabilized from approximately $2 \times 10^{-5}$ Hz$^{-1/2}$ to a minimum value of $1.6 \times 10^{-7}$ Hz$^{-1/2}$, corresponding to the power noise reduction by a factor of $125$. The bandwidth at which stabilization occurs ranges from $400$ Hz to $100$ kHz. The work reported in this letter further paves the way for high power laser stability techniques which could be implemented in optomechanical experiments and in gravitational wave detectors

Applications of Stochastic Optimization and Machine Learning in Photonic Nanostructures and Quantum Optical Systems

Recent advances in stochastic optimization and machine learning methods, along with successful innovative applications across a wide variety of fields, promise game-changing impacts, potentially resulting in new, intelligent development and design tools for nanophotonic devices and optical systems with more diverse and better functionalities. The research which has been carried out in this dissertation addresses three such innovative approaches and novel designs. There has been an explosion of interest in graphene for photonic applications, as it provides a degree of freedom to manipulate electromagnetic waves. The first part of the research in this dissertation develops a micro-genetic global optimization algorithm and designs graphene-based nanophotonic structures that enable electrically selective, switchable, and tunable thermal emitters. This study may contribute towards the realization of wavelength-selective detectors with switchable intensity for sensing applications. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has opened a new window to the universe by detecting the first gravitational waves in 2015. The discovery impels the need for better detection schemes by decreasing the limiting noise sources in gravitational-wave interferometers. The second part of the research in this dissertation employs the genetic algorithm to design optimal mechanical microresonators to minimize thermal noise below the standard quantum limit (SQL) in gravitational wave detectors. The proposed microresonator allows it to serve as a testbed for quantum non-demolition measurements, and to open new regimes of precision measurement that are relevant for many practical sensing applications, including advanced gravitational wave detectors. Laser beam profiling is necessary for most laser applications, and enabling automated detection of orbital angular momentum (OAM) can tremendously contribute to quantum optical xvii experiments. The third part of the research in this dissertation develops the convolutional neural network (CNN) models to automatically identify and classify the noisy images of LG modes collected from two different experimental setups. The classification performance measures of the CNN models are studied for generalizing and adapting to experimental conditions. This study may contribute towards enabling OAM light with increased degrees of freedom and thereby its various applications in telecommunications, sensing, and high-resolution imaging systems