Abstract
In this paper, application of adaptive neuro-fuzzy inference system (ANFIS) in modeling of CMOS logic gates as a tool in designing and simulation of CMOS logic circuits is presented. Structures of the ANFIS are developed and trained in MATLAB 7.0.4 program. We have used real hardware data for training the ANFIS network. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training. Influence of the structure of the proposed ANFIS model on accuracy and network performance has been analyzed through some combinational circuits. For the comparison of the ANFIS simulation results, we have simulated the circuits in HSPICE environment with 0.35μm process nominal parameters. The comparison between ANFIS, HSPICE, and real hardware shows the feasibility and accuracy of the proposed ANFIS modeling procedure. The results show the proposed ANFIS simulation has much higher speed and accuracy in comparison with HSPICE simulation and it can be simply used in software tools for designing and simulation of complex CMOS logic circuits.
Published Version
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