Title :
ANN based modeling, testing and diagnosis of MEMS [capacitive pressure transducer example]
Author :
Litovski, Vanco ; Andrejevic, Miona ; Zwolinski, Mark
Author_Institution :
Fac. of Electron. Eng., Nis Univ., Serbia
Abstract :
New concepts of simulation, testing and diagnosis of MEMS are proposed, intended to boost the time to market and dependability of such systems. Black-box modeling of nonelectronic parts is introduced using artificial neural networks, so enabling radically faster simulation without concurrent algorithms and parallel computation. A lumped model of the capacitive transducer, being the part of a micro-electro-mechanical capacitive pressure sensing system, is created using an ANN. Faults are then introduced to the sensing system and simulation of the fault-free and faulty circuits are demonstrated.
Keywords :
capacitive sensors; circuit simulation; circuit testing; design for manufacture; fault simulation; lumped parameter networks; micromechanical devices; microsensors; neural nets; pressure sensors; ANN based modeling; DFM; MEMS diagnosis; MEMS simulation; MEMS testing; artificial neural networks; capacitive pressure transducer; fault simulation; nonelectronic parts black-box modeling; transducer lumped model; Artificial neural networks; Circuit faults; Circuit simulation; Computational modeling; Computer networks; Concurrent computing; Micromechanical devices; System testing; Time to market; Transducers;
Conference_Titel :
Neural Network Applications in Electrical Engineering, 2004. NEUREL 2004. 2004 7th Seminar on
Print_ISBN :
0-7803-8547-0
DOI :
10.1109/NEUREL.2004.1416568
