Predicting the mechanical behavior of self-deformed micromachined cantilevers

Pasupuleti, Ajay; Sahin, Ferat; Raisanen, A.; Hebding, Jeremiah; Walter, Wayne W.; Hirschman, K.D.

doi:10.1109/esime.2005.1502810

Cited by 1 publication

(8 citation statements)

References 9 publications

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“…Methodology As discussed in the previous section, mechanical behavior models for thin film materials are still at their infancy. To this effect, an alternative approach was proposed [16]. Fig.…”

Section: Proposed Techniquementioning

confidence: 99%

“…Empirical models are then extracted by correlating the algebraic equations and the FEA results for a large number of data sets. Please refer to our previous work for more information [16][17]. The Simulations were performed in the two-dimensional structural analysis mode using the Plane 82 solid element.…”

Section: Proposed Techniquementioning

confidence: 99%

“…Among the various techniques, empirical estimation techniques that are based on non-parametric algorithms have been proved to be the most effective [16]. This is because these techniques are able to relate the loading parameters, material properties, fabrication induced parameters, and geometry of the microstructures with their performance characteristics with great accuracy [16].…”

Section: Introductionmentioning

confidence: 99%

“…Given the complexity in the relationship between the device dimensions and fabrication associated parameters, many researchers are looking at analytical solutions that can learn and predict this behavior [15][16][17]. Among the various techniques, empirical estimation techniques that are based on non-parametric algorithms have been proved to be the most effective [16]. This is because these techniques are able to relate the loading parameters, material properties, fabrication induced parameters, and geometry of the microstructures with their performance characteristics with great accuracy [16].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, there is a need for new techniques that can estimate the Young's Modulus of thin films. Given the complexity in the relationship between the device dimensions and fabrication associated parameters, many researchers are looking at analytical solutions that can learn and predict this behavior [15][16][17]. Among the various techniques, empirical estimation techniques that are based on non-parametric algorithms have been proved to be the most effective [16].…”

Section: Introductionmentioning

confidence: 99%

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Soft Computing Techniques for Determining the Effective Young's Modulus of Materials in Thin Films

Pasupuleti

Sahin

2006

2006 IEEE International Conference on Systems, Man and Cybernetics

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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.

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Section: Proposed Techniquementioning

confidence: 99%

Section: Proposed Techniquementioning

confidence: 99%