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.

Date of creation, presentation, or exhibit



©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ISBN: 1-4244-0099-6Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Microelectronic Engineering (KGCOE)


RIT – Main Campus