Non-sorting genetic algorithm in the optimization of unity-gain cells

Non-sorting genetic algorithm in the optimization of unity-gain cells

An optimization system based on the multi-objective evolutionary technique NSGA-II is presented to automatically size unity-gain cells, namely: voltage and current followers, and voltage and current mirrors. These unity-gain cells are optimized in three performance objectives: gain, bandwidth and of...

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Hauptverfasser: Guerra-Gomez, I., Tlelo-Cuautle, E., Reyes-Garcia, C.A., Reyes-Salgado, G., de la Fraga, L.G.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Carbon capture and storage
Circuit simulation
Circuit synthesis
Cities and towns
Computer science
Genetic algorithms
Mirrors
Performance gain
User-generated content
Voltage
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Beschreibung
Zusammenfassung:An optimization system based on the multi-objective evolutionary technique NSGA-II is presented to automatically size unity-gain cells, namely: voltage and current followers, and voltage and current mirrors. These unity-gain cells are optimized in three performance objectives: gain, bandwidth and offset. The proposed optimization system uses HSPICE as circuit evaluator by including input and output resistances as constraints, besides by guaranteeing that all transistors are in saturation operation.
DOI:10.1109/ICEEE.2009.5393478