Optical neural networks: an introduction to a special issue by the feature editors

This feature of Applied Optics is devoted to papers on the optical implementation of neural-network models of computation. Papers are included on optoelectronic neuron array devices, optical interconnection techniques using holograms and spatial light modulators, optical associative memories, demonstrations of optoelectronic systems for learning, classification, and target recognition, and on the demonstration, analysis, and simulation of adaptive interconnections for optical neural networks using photorefractive volume holograms

Polariton Nanophotonics using Phase Change Materials

Polaritons formed by the coupling of light and material excitations such as plasmons, phonons, or excitons enable light-matter interactions at the nanoscale beyond what is currently possible with conventional optics. Recently, significant interest has been attracted by polaritons in van der Waals materials, which could lead to applications in sensing, integrated photonic circuits and detectors. However, novel techniques are required to control the propagation of polaritons at the nanoscale and to implement the first practical devices. Here we report the experimental realization of polariton refractive and meta-optics in the mid-infrared by exploiting the properties of low-loss phonon polaritons in isotopically pure hexagonal boron nitride (hBN), which allow it to interact with the surrounding dielectric environment comprising the low-loss phase change material, Ge$_3$Sb$_2$Te$_6$ (GST). We demonstrate waveguides which confine polaritons in a 1D geometry, and refractive optical elements such as lenses and prisms for phonon polaritons in hBN, which we characterize using scanning near field optical microscopy. Furthermore, we demonstrate metalenses, which allow for polariton wavefront engineering and sub-wavelength focusing. Our method, due to its sub-diffraction and planar nature, will enable the realization of programmable miniaturized integrated optoelectronic devices, and will lay the foundation for on-demand biosensors.Comment: 15 pages, 4 figures, typos corrected in v

Optoelectronic devices and packaging for information photonics

This thesis studies optoelectronic devices and the integration of these components onto optoelectronic multi chip modules (OE-MCMs) using a combination of packaging techniques. For this project, (1×12) array photodetectors were developed using PIN diodes with a GaAs/AlGaAs strained layer structure. The devices had a pitch of 250μm, operated at a wavelength of 850nm. Optical characterisation experiments of two types of detector arrays (shoe and ring) were successfully performed. Overall, the shoe devices achieved more consistent results in comparison with ring diodes, i.e. lower dark current and series resistance values. A decision was made to choose the shoe design for implementation into the high speed systems demonstrator. The (1x12) VCSEL array devices were the optical sources used in my research. This was an identical array at 250μm pitch configuration used in order to match the photodetector array. These devices had a wavelength of 850nm. Optoelectronic testing of the VCSEL was successfully conducted, which provided good beam profile analysis and I-V-P measurements of the VCSEL array. This was then implemented into a simple demonstrator system, where eye diagrams examined the systems performance and characteristics of the full system and showed positive results. An explanation was given of the following optoelectronic bonding techniques: Wire bonding and flip chip bonding with its associated technologies, i.e. Solder, gold stud bump and ACF. Also, technologies, such as ultrasonic flip chip bonding and gold micro-post technology were looked into and discussed. Experimental work implementing these methods on packaging the optoelectronic devices was successfully conducted and described in detail. Packaging of the optoelectronic devices onto the OEMCM was successfully performed. Electrical tests were successfully carried out on the flip chip bonded VCSEL and Photodetector arrays. These results verified that the devices attached on the MCM achieved good electrical performance and reliable bonding. Finally, preliminary testing was conducted on the fully assembled OE-MCMs. The aim was to initially power up the mixed signal chip (VCSEL driver), and then observe the VCSEL output

Digital neural circuits : from ions to networks

PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work