In this research a generalized software framework that enables accurate computer aided design of MEMS devices is developed. The proposed simulation engine utilizes a novel material property estimation technique that generates effective material properties at the microscopic level. The material property models were developed based on empirical analysis and the behavior extraction of standard test structures. A literature review is provided on the physical phenomena that govern the mechanical behavior of thin films materials. This survey indicates that the present day models operate under a wide range of assumptions that may not be applicable to the micro-world. Thus, this methodology is foreseen to be an essential tool for MEMS designers as it would develop empirical models that relate the loading parameters, material properties, and the geometry of the microstructures with its performance characteristics. This process involves learning the relationship between the above parameters using non-parametric learning algorithms such as radial basis function networks and genetic algorithms. The proposed simulation engine has a graphical user interface (GUI) which is very adaptable, flexible, and transparent. The GUI is able to encompass all parameters associated with the determination of the desired material property so as to create models that provide an accurate estimation of the desired property. This technique was verified by fabricating and simulating bilayer cantilevers consisting of aluminum and glass (TEOS oxide) in our previous work. The results obtained were found to be very encouraging.
Diffractive optical elements (DOE) utilize diffraction to manipulate light in optical systems. These elements have a wide range of applications including optical interconnects, coherent beam addition, laser beam shaping and refractive optics aberration correction. Due to the wide range of applications optimal design of DOE has become an important research problem. In the design of the DOEs, existing techniques utilize the Fresnel diffraction theory to compute the phase at the desired location at the output plane. Since this process involves solving nonlinear integral equations, various numerical methods along with robust optimization algorithms have been proposed. However all the algorithms proposed so far assume that the size and the spacing of the elements as independent variables in the design of optimal diffractive gratings. Therefore search algorithms need to be called every time the required geometry of the elements changes, resulting in a computationally expensive design procedure for systems utilizing a large number of DOEs.
In this work, we have developed a novel algorithm that uses neural networks with multiple hidden layers to overcome this limitation and arrives at a general solution for the design of the DOEs for a given application. Inputs to this network are the spacing between the elements and the input/output planes. The network outputs the phase gratings that are required to obtain the desired intensity at the specified location in the output plane. The network was trained using the back-propagation technique. The training set was generated by using genetic algorithm approach as described in literature. The mean square error obtained is comparable to conventional techniques but with much lower computational costs.
In this paper we propose an adaptive routing using a fuzzy system. The traffic in the network is re-routed to nodes, which are less congested, or have spare capacity. Based on a set of fuzzy rules, link cost is dynamically assigned depending upon the present condition of the network. Distance vector algorithm, which is one of the shortest path routing algorithms is used to build the routing tables at each node in the network. The proposed fuzzy system determines the link cost given the present congestion situation measured via the delays experienced in the network and the offered load on the network. Delay in the links, was estimated by the time taken for the test packets to travel from the node to its neighbors. The delay information collected by the test packets and the number of packets waiting in the queue, are the two inputs to the fuzzy system. The output of the fuzzy system is the link cost. This algorithm was applied on a simulated NSFNET, the USA backbone, as well as to another test network with a different topology. Robustness and optimality of the proposed fuzzy system was tested by simulating various types of load patterns on these networks. Simulation studies showed that the performance of the fuzzy system was very close to or better than the best performance of the composite metric under different load conditions and topologies.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.