Yield-aware multi-objective optimization of a MEMS accelerometer system using QMC-based methodologies
| Autor: | Gunhan Dundar, Murat Pak, Francisco V. Fernández |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Microelectromechanical systems Computer science Capacitive sensing 020208 electrical & electronic engineering General Engineering 02 engineering and technology Mems sensors Accelerometer 01 natural sciences Multi-objective optimization Hardware_GENERAL 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Electronic engineering Quasi-Monte Carlo method |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname |
| Popis: | This paper proposes a novel yield-aware optimization methodology that can be used for mixed-domain synthesis of robust micro-electro-mechanical systems (MEMS). The robust Pareto front optimization of a MEMS accelerometer system, which includes a capacitive MEMS sensor and an analog read-out circuitry, is realized by co-optimization of the mixed-domain system where the sensor performances are evaluated using highly accurate analytical models and the circuit level simulations are carried out by an electrical simulator. Two different approaches for yield-aware optimization have been implemented in the synthesis loop. The Quasi Monte Carlo (QMC) technique has been used to embed the variation effects into the optimization loop. The results for both two- and three-dimensional yield-aware optimization are quite promising for robust MEMS accelerometer synthesis. |
| Databáze: | OpenAIRE |
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