3D Micromachining of Optical Devices on Transparent Material by Ultrafast Laser

Ultrafast lasers, also referred to as ultrashort pulse lasers, have played an important role in the development of next generation manufacturing technologies in recent years. Their broad range of applications has been investigated in the field of microstructure processing for the biomedical, optical, and many other laboratory and industrial fields. Ultrafast laser machining has numerous unique advantages, including high precision, a small heat affected area, high peak intensity, 3D direct-writing, and other flexible capabilities When integrated with optical delivery, motion devices and control systems, one-step fabrication of assemble-free micro-devices can be realized. In particular, ultrafast lasers enable the creation of various three-dimensional, laser-induced modifications using an extremely high peak intensity over a short time frame, producing precise ablation of material and a small heat affected area in transparent materials. In contrast, lasers with longer pulse durations are based on a thermal effect, which results in significant melting in the heat affected area. In general, ultrafast laser micromachining can be used either to subtract material from or to change the material properties of both absorptive and transparent substances. Recently, integrated micro-devices including optical fiber sensors, microfluidic devices, and lab-on-chips (LOC) have gained worldwide recognition because of their unique characteristics. These micro-devices have been widely used for a broad range of applications, from fundamental research to industry. The development of integrated glass micro-devices introduced new possibilities for biomedical, environmental, civil and other industries and research areas. Of these devices, optical fiber sensors are recognized for their small size, accuracy, resistance to corrosion, fast response and high integration. They have demonstrated their excellent performance in sensing temperature, strain, refractive index and many other physical quantities. In addition to the all-in-fiber device, the LOC is another attractive candidate for use in micro-electro-mechanical systems (MEMS) because it includes several laboratory functions on a single integrated circuit. LOCs provide such advantages as low fluid volume consumption, improved analysis and response times due to short diffusion distances, and better process control, all of which are specific to their application. Combining ultrafast laser micromachining techniques with integrated micro-devices has resulted in research on a variety of fabrication methods targeted for particular purposes. In this dissertation, the direct creation of three-dimensional (3D) structures using an ultra-fast laser was investigated for use in optical devices. This research was motivated by the desire to understand more fully the relationship among laser parameters, material properties and 3D optical structures. Various all-in-fiber sensors in conjunction with femtosecond laser ablation and irradiation were investigated based on magnetic field, temperature and strain application. An incoherent optical carrier based microwave interferometry technique was used for in-situ weak reflector fabrication and a picosecond laser micromachining technique was introduced for developing LOCs with unlimited utilization potential

GPU-based implementation of real-time system for spiking neural networks

Real-time simulations of biological neural networks (BNNs) provide a natural platform for applications in a variety of fields: data classification and pattern recognition, prediction and estimation, signal processing, control and robotics, prosthetics, neurological and neuroscientific modeling. BNNs possess inherently parallel architecture and operate in continuous signal domain. Spiking neural networks (SNNs) are type of BNNs with reduced signal dynamic range: communication between neurons occurs by means of time-stamped events (spikes). SNNs allow reduction of algorithmic complexity and communication data size at a price of little loss in accuracy. Simulation of SNNs using traditional sequential computer architectures results in significant time penalty. This penalty prohibits application of SNNs in real-time systems. Graphical processing units (GPUs) are cost effective devices specifically designed to exploit parallel shared memory-based floating point operations applied not only to computer graphics, but also to scientific computations. This makes them an attractive solution for SNN simulation compared to that of FPGA, ASIC and cluster message passing computing systems. Successful implementations of GPU-based SNN simulations have been already reported. The contribution of this thesis is the development of a scalable GPU-based realtime system that provides initial framework for design and application of SNNs in various domains. The system delivers an interface that establishes communication with neurons in the network as well as visualizes the outcome produced by the network. Accuracy of the simulation is emphasized due to its importance in the systems that exploit spike time dependent plasticity, classical conditioning and learning. As a result, a small network of 3840 Izhikevich neurons implemented as a hybrid system with Parker-Sochacki numerical integration method achieves real time operation on GTX260 device. An application case study of the system modeling receptor layer of retina is reviewed