Workshops at IMS2023

Lists future events that should be of interest to practitioners and researchers.Peer ReviewedPostprint (published version

Agenda: Second International Workshop on Thin Films for Electronics, Electro-Optics, Energy and Sensors (TFE3S)

University of Dayton’s Center of Excellence for Thin Film Research and Surface Engineering (CETRASE) is delighted to organize its second international workshop at the University of Dayton’s Research Institute (UDRI) campus in Dayton, Ohio, USA. The purpose of the new workshop is to exchange technical knowledge and boost technical and educational collaboration activities within the thin film research community through our CETRASE and the UDRI

A review of technologies and design techniques of millimeter-wave power amplifiers

his article reviews the state-of-the-art millimeter-wave (mm-wave) power amplifiers (PAs), focusing on broadband design techniques. An overview of the main solid-state technologies is provided, including Si, gallium arsenide (GaAs), GaN, and other III-V materials, and both field-effect and bipolar transistors. The most popular broadband design techniques are introduced, before critically comparing through the most relevant design examples found in the scientific literature. Given the wide breadth of applications that are foreseen to exploit the mm-wave spectrum, this contribution will represent a valuable guide for designers who need a single reference before adventuring in the challenging task of the mm-wave PA design

Electronic Photonic Integrated Circuits and Control Systems

Photonic systems can operate at frequencies several orders of magnitude higher than electronics, whereas electronics offers extremely high density and easily built memories. Integrated photonic-electronic systems promise to combine advantage of both, leading to advantages in accuracy, reconfigurability and energy efficiency. This work concerns of hybrid and monolithic electronic-photonic system design. First, a high resolution voltage supply to control the thermooptic photonic chip for time-bin entanglement is described, in which the electronics system controller can be scaled with more number of power channels and the ability to daisy-chain the devices. Second, a system identification technique embedded with feedback control for wavelength stabilization and control model in silicon nitride photonic integrated circuits is proposed. Using the system, the wavelength in thermooptic device can be stabilized in dynamic environment. Third, the generation of more deterministic photon sources with temporal multiplexing established using field programmable gate arrays (FPGAs) as controller photonic device is demonstrated for the first time. The result shows an enhancement to the single photon output probability without introducing additional multi-photon noise. Fourth, multiple-input and multiple-output (MIMO) control of a silicon nitride thermooptic photonic circuits incorporating Mach Zehnder interferometers (MZIs) is demonstrated for the first time using a dual proportional integral reference tracking technique. The system exhibits improved performance in term of control accuracy by reducing wavelength peak drift due to internal and external disturbances. Finally, a monolithically integrated complementary metal oxide semiconductor (CMOS) nanophotonic segmented transmitter is characterized. With segmented design, the monolithic Mach Zehnder modulator (MZM) shows a low link sensitivity and low insertion loss with driver flexibility

Development of a Dual-Mode CMOS Microelectrode Array for the Simultaneous Study of Electrochemical and Electrophysiological Activities of the Brain

Medical diagnostic devices are in high demand due to increasing cases of neurodegenerative diseases in the aging population and pandemic outbreaks in our increasingly connected global community. Devices capable of detecting the presence of a disease in its early stages can have dramatic impacts on how it can be treated or eliminated. High cost and limited accessibility to diagnostic tools are the main barriers preventing potential patients from receiving a timely disease diagnosis. This dissertation presents several devices that are aimed at providing higher quality medical diagnostics at a low cost. Brain function is commonly studied with systems detecting the action potentials that are formed when neurons fire. CMOS technology enables extremely high-density electrode arrays to be produced with integrated amplifiers for high-throughput action potential measurement systems while greatly reducing the cost per measurement compared to traditional tools. Recently, CMOS technology has also been used to develop high-throughput electrochemical measurement systems. While action potentials are important, communication between neurons occurs by the flow of neurotransmitters at the synapses, so measurement of action potentials alone is incapable of fully studying neurotransmission. In many neurodegenerative diseases the breakdown in neurotransmission begins well before the disease manifests itself. The development of a dual-mode CMOS device that is capable of simultaneous high-throughput measurement of both action potentials and neurotransmitter flow via an on-chip electrode array is presented in this dissertation. This dual-mode technology is useful to those studying the dynamic decay of the neurotransmission process seen in many neurodegenerative diseases using a low-cost CMOS chip. This dissertation also discusses the development of more traditional diagnostic devices relying on PCR, a method commonly used only in centralized laboratories and not readily available at the point-of-care. These technologies will enable faster, cheaper, more accurate, and more accessible diagnostics to be performed closer to the patient

Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits

The need for products that are more streamlined, more useful, and have longer battery lives is rising in today's culture. More components are being integrated onto smaller, more complex chips in order to do this. The outcome is higher total power consumption as a result of increased power dissipation brought on by dynamic and static currents in integrated circuits (ICs). For effective power planning and the precise application of power pads and strips by floor plan engineers, estimating power dissipation at an early stage is essential. With more information about the design attributes, power estimation accuracy increases. For a variety of applications, including function approximation, regularization, noisy interpolation, classification, and density estimation, they offer a coherent framework. RBFNN training is also quicker than training multi-layer perceptron networks. RBFNN learning typically comprises of a linear supervised phase for computing weights, followed by an unsupervised phase for determining the centers and widths of the Gaussian basis functions. This study investigates several learning techniques for estimating the synaptic weights, widths, and centers of RBFNNs. In this study, RBF networks—a traditional family of supervised learning algorithms—are examined.  Using centers found using k-means clustering and the square norm of the network coefficients, respectively, two popular regularization techniques are examined. It is demonstrated that each of these RBF techniques are capable of being rewritten as data-dependent kernels. Due to their adaptability and quicker training time when compared to multi-layer perceptron networks, RBFNNs present a compelling option to conventional neural network models. Along with experimental data, the research offers a theoretical analysis of these techniques, indicating competitive performance and a few advantages over traditional kernel techniques in terms of adaptability (ability to take into account unlabeled data) and computing complexity. The research also discusses current achievements in using soft k-means features for image identification and other tasks

Hybrid integration methods for on-chip quantum photonics

The goal of integrated quantum photonics is to combine components for the generation, manipulation, and detection of nonclassical light in a phase-stable and efficient platform. Solid-state quantum emitters have recently reached outstanding performance as single-photon sources. In parallel, photonic integrated circuits have been advanced to the point that thousands of components can be controlled on a chip with high efficiency and phase stability. Consequently, researchers are now beginning to combine these leading quantum emitters and photonic integrated circuit platforms to realize the best properties of each technology. In this paper, we review recent advances in integrated quantum photonics based on such hybrid systems. Although hybrid integration solves many limitations of individual platforms, it also introduces new challenges that arise from interfacing different materials. We review various issues in solid-state quantum emitters and photonic integrated circuits, the hybrid integration techniques that bridge these two systems, and methods for chip-based manipulation of photons and emitters. Finally, we discuss the remaining challenges and future prospects of on-chip quantum photonics with integrated quantum emitters. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen