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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. This process involves solving nonlinear integral equations for which various numerical methods along with robust optimization algorithms exist in literature. 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 possibly 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 GS algorithm approach as described in literature. The mean square error obtained is comparable to conventional techniques but with much lower computational costs.

This research aims at characterizing and predicting the Young's Modulus of thin film materials that are utilized in the Microelectromechanical systems (MEMS). As a proof of concept, aluminum and TEOS thin films were analyzed using bilayer cantilever as a test structure. Due to the lack of understanding of the mechanical behavior of thin film materials in the micro-scale domain, empirical models were developed that utilize soft computing techniques. As a result, this methodology is foreseen to be an essential tool for MEMS designers as it can estimate and predict effective Young's modulus of materials in the micro-scale domain. In the estimation phase, 2D search and micro genetic algorithm were studied and in the prediction phase, back propagation based neural networks and One Dimensional Radial Basis Function Networks (1D-RBFN) were studied. All combinations of these soft computing techniques are evaluated. Based on the results, we conclude that among the various combinations tested, the combination of 1D-RBFN (prediction phase) and GA (estimation phase) presented the best results. Research is in progress in applying other algorithms such as support vector machines as well as investigating other novel test structures that can be used to extract other material properties such as coefficient of thermal expansion.

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.