TY - GEN
T1 - On Balanced Tradeoffs between Stiffness and Design Area in CMOS-MEMS Accelerometer Springs
AU - Granados-Rojas, Benito
AU - Reyes-Barranca, Mario Alfredo
AU - Flores-Nava, Luis Martin
AU - Abarca-Jimenez, Griselda Stephany
AU - Aleman-Arce, Miguel Angel
AU - Gonzalez-Navarro, Yesenia Eleonor
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/11
Y1 - 2020/11/11
N2 - This work shows the application of an evolutionary algorithm to find a balanced tradeoff between the design area occupied by the layout of a metal folded beam spring and its stiffness, in CMOS-MEMS inertial sensor developments.CMOS-MEMS is the set of techniques aiming to integrate micro-electromechanical structures with a variety of purposes ranging from purely capacitive to thermal and radiation-sensitive applications within the metal and semiconductor layers found in most CMOS standard fabrication processes. This integration allows to create monolithic systems where transducers and signal processing stages coexist.The algorithm used to analyze the mentioned relations can be characterized as a genetic algorithm with three variables and two objectives functions, since these two objective (goals) are in conflict, the acquisition and discussion of results will be driven by the Pareto Optimality criteria. The solutions given by the execution of the algorithm may be taken as a start point to fine-Tune the final design according to the designer preferences.
AB - This work shows the application of an evolutionary algorithm to find a balanced tradeoff between the design area occupied by the layout of a metal folded beam spring and its stiffness, in CMOS-MEMS inertial sensor developments.CMOS-MEMS is the set of techniques aiming to integrate micro-electromechanical structures with a variety of purposes ranging from purely capacitive to thermal and radiation-sensitive applications within the metal and semiconductor layers found in most CMOS standard fabrication processes. This integration allows to create monolithic systems where transducers and signal processing stages coexist.The algorithm used to analyze the mentioned relations can be characterized as a genetic algorithm with three variables and two objectives functions, since these two objective (goals) are in conflict, the acquisition and discussion of results will be driven by the Pareto Optimality criteria. The solutions given by the execution of the algorithm may be taken as a start point to fine-Tune the final design according to the designer preferences.
KW - CMOS-MEMS
KW - EMOO
KW - Genetic Algorithm
KW - Inertial Sensor
KW - MEMS
KW - Stiffness
UR - http://www.scopus.com/inward/record.url?scp=85099478475&partnerID=8YFLogxK
U2 - 10.1109/CCE50788.2020.9299117
DO - 10.1109/CCE50788.2020.9299117
M3 - Contribución a la conferencia
AN - SCOPUS:85099478475
T3 - 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2020
BT - 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2020
Y2 - 11 November 2020 through 13 November 2020
ER -