Proceedings of the conference on Design, automation and test in Europe - Volume 1
Fast and accurate parasitic capacitance models for layout-aware
Proceedings of the 41st annual Design Automation Conference
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
A combined feasibility and performance macromodel for analog circuits
Proceedings of the 42nd annual Design Automation Conference
Performance space modeling for hierarchical synthesis of analog integrated circuits
Proceedings of the 42nd annual Design Automation Conference
Adaptive sampling and modeling of analog circuit performance parameters with pseudo-cubic splines
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
GA-SVM feasibility model and optimization kernel applied to analog IC design automation
Proceedings of the 17th ACM Great Lakes symposium on VLSI
Analog circuits optimization based on evolutionary computation techniques
Integration, the VLSI Journal
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 2009 International Conference on Computer-Aided Design
Analog Integrated Circuits and Signal Processing
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Fast and accurate performance estimation methods are essential to automated synthesis of analog circuits. Development of analog performance models is difficult due to the highly nonlinear nature of various analog performance parameters. This paper presents a neural network-based methodology for creating fast and efficient models for estimating the performance parameters of CMOS operational amplifier topologies. Effective methods for generation and use of the training data are proposed to enhance the accuracy of the neural models. The efficiency and accuracy of the resulting performance models are demonstrated via their use in a genetic algorithm-based circuit synthesis system. The genetic synthesis tool optimizes a fitness function based on user-specified performance constraints. The performance parameters of the synthesized circuits are validated by SPICE simulations and compared with those predicted by the neural network models. Experimental studies demonstrate that neural network modeling is an effective, fast, and accurate methodology for performance estimation.