Signature analysis for MEMS pseudorandom testing using neural networks

Autor: Kupka, L., Simeu, E., Stratigopoulos, H. -G, Libor Rufer, Mir, S., Tůmová, O.
Přispěvatelé: Laboratoire pour l'utilisation du rayonnement électromagnétique (LURE), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-MENRT-Centre National de la Recherche Scientifique (CNRS), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Torella, Lucie, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)
Jazyk: angličtina
Rok vydání: 2008
Předmět:
Zdroj: 12th IMEKO TC1 & TC7 Joint Symposium on Man Science & Measurement
12th IMEKO TC1 & TC7 Joint Symposium on Man Science & Measurement, Sep 2008, Annecy, France
Scopus-Elsevier
Popis: The aim of this work is to develop a low-overhead, low-cost built-in test for Micro Electro Mechanical Systems (MEMS). The proposed method relies on processing the Impulse Response (IR) through trained neural networks, in order to predict a set of MEMS performances, which are otherwise very expensive to measure using the conventional test approach. The use of neural networks allows us to employ a low-dimensional IR signature, which results in a compact built-in test. A MEMS structure combining electro-thermal excitation and piezoresistive sensing was chosen as our case study. A behavioral model of this structure was built using Matlab for the purpose of the experiment. The results demonstrate that the neural network predictions are in excellent agreement with the simulation results of the behavioral model.
Databáze: OpenAIRE
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