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Failure Modes Model of MEMS Accelerometers Based on Neural Networks

[+] Author Affiliations
Yanping Bai, Ping An, Yilong Hao

Peking University, Beijing, China

Paper No. MicroNano2008-70104, pp. 767-771; 5 pages
doi:10.1115/MicroNano2008-70104
From:
  • 2008 Second International Conference on Integration and Commercialization of Micro and Nanosystems
  • 2008 Second International Conference on Integration and Commercialization of Micro and Nanosystems
  • Clear Water Bay, Kowloon, Hong Kong, June 3–5, 2008
  • Conference Sponsors: Nanotechnology Institute
  • ISBN: 0-7918-4294-0 | eISBN: 0-7918-3819-6
  • Copyright © 2008 by ASME

abstract

Fabrication of a MEMS system involves design, testing, packaging and reliability related issues. However, reliability issues that are discovered at a late phase may cause major delays in the product development going together with high costs. In this paper we study the failure modes and Mechanisms of MEMS accelerometers products and present the classification modeling of failure modes based on neural networks. In ours MEMS accelerometers, there are six failure mechanisms that have been found to be the primary sources of failure nodes. We introduce nonlinear BP network with a hidden layer and linear perception to classify for MEMS accelerometers products. Classification results show that nonlinear BP network seem to be most appropriate to approach the problem of failure modes classification than linear perception. BP neural network is capable of learning the intrinsic relations of the patterns with which they were trained. For all experiments results, the training success of rate is 100% for both methods. BP networks obtained a high forecast success of rate of over 99.5%. The linear perception model obtained a success of rate of over 95.5%. We also analyze the technology stability of MEMS products.

Copyright © 2008 by ASME

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