Particle swarm optimization based synthesis of analog circuits using neural network performance macromodels
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This thesis presents an efficient an fast synthesis procedure for an analog circuit. The proposed synthesis procedure used artificial neural network (ANN) models in combination with particle swarm optimizer. ANN has been used to develop macro-models for SPICE simulated data of analog circuit which takes transistor sizes as input and produced circuit specification as output in negligible time. The particle swarm optimizer explore the specfied design space and generates transistor sizes as potential solutions. Several synthesis results are presented which show good accuracy with respect to SPICE simulations. Since the proposed procedure does not require an SPICE simulation in the synthesis loop, it substantially reduces the design time in circuit design optimization.
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