Neural network-based design approach for submicron MOS integrated circuits

Authors:
M. Avci
Affiliations:
University of Cukurova, Computer Engineering Department, Balcali, Adana, Turkey
Venue:
Mathematics and Computers in Simulation
Year:
2008

Citing 3
Cited 2

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MOSFET modeling with SPICE: principles and practice
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CMOS: Circuit Design, Layout, and Simulation (IEEE Press Series on Microelectronic Systems)
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Abstract

In this work, a neural network-based solution to BSIM3v3 MOSFET model is developed to find the most suitable channel parameters to improve the production yield and operation accuracy of submicron integrated circuits. By means of the proposed solution the channel parameters of each transistor can be found using terminal voltages and the drain current. The training data of the developed neural network are obtained by various simulations in the HSPICE design environment with TSMC 0.18@m and AMIS 0.5@m process parameters. The neural network structure is developed and trained in MATLAB 6.0 environment. The efficiency of the proposed neural network-based model is tested on different analog integrated circuits.