Generation of Electrically Induced Stimuli for MEMS Self-Test
Journal of Electronic Testing: Theory and Applications
Test Generation for Accurate Prediction of Analog Specifications
VTS '00 Proceedings of the 18th IEEE VLSI Test Symposium
Multi-Modal Built-In Self-Test for Symmetric Microsystems
VTS '04 Proceedings of the 22nd IEEE VLSI Test Symposium
Alternate Electrical Tests for Extracting Mechanical Parameters of MEMS Accelerometer Sensors
VTS '06 Proceedings of the 24th IEEE VLSI Test Symposium
A novel method for test and calibration of capacitive accelerometers with a fully electrical setup
DDECS '08 Proceedings of the 2008 11th IEEE Workshop on Design and Diagnostics of Electronic Circuits and Systems
Evaluation of a fully electrical test and calibration method for MEMS capacitive accelerometers
IMS3TW '08 Proceedings of the 2008 IEEE 14th International Mixed-Signals, Sensors, and Systems Test Workshop
A method for electrical calibration of MEMS accelerometers through multivariate regression
IMS3TW '09 Proceedings of the 2009 IEEE 15th International Mixed-Signals, Sensors, and Systems Test Workshop
A Parallel Tester Architecture for Accelerometer and Gyroscope MEMS Calibration and Test
Journal of Electronic Testing: Theory and Applications
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This paper deals with the development of a fully electrical method to estimate the sensitivity of capacitive MEMS accelerometers in batch fabrication. The objectives are to test and calibrate them without the need of costly and time-consuming mechanical test equipments. Alternate electrical test measurements are introduced and two methods are evaluated. The first one is based on the analytical study of the relations between the electrical test parameters and the sensitivity. The second one is based on a regression analysis of these relations on a learning batch of sensors. Both are evaluated on a case study accelerometer with Monte Carlo simulations. Applying the methods for calibration shows that the dispersion of the sensitivity can be improved. Results show that the second method is more promising.