Neural Network Based MOS Transistor Geometry Decision for TSMC 0.18μ Process Technology

  • Mutlu Avci
  • Tulay Yildirim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)


In sub-micron technologies MOSFETs are modeled by complex nonlinear equations. These equations include many process parameters, terminal voltages of the transistor and also the transistor geometries; channel width (W) and length (L) parameters. The designers have to choose the most suitable transistor geometries considering the critical parameters, which determine the DC and AC characteristics of the circuit. Due to the difficulty of solving these complex nonlinear equations, the choice of appropriate geometry parameters depends on designer’s knowledge and experience. This work aims to develop a neural network based MOSFET model to find the most suitable channel parameters for TSMC 0.18μ technology, chosen by the circuit designer. The proposed model is able to find the channel parameters using the input information, which are terminal voltages and the drain current. The training data are obtained by various simulations in the HSPICE design environment with TSMC 0.18μm process nominal parameters. The neural network structure is developed and trained in the MATLAB 6.0 program. To observe the utility of proposed MOSFET neural network model it is tested through two basic integrated circuit blocks.


Drain Current Hide Unit Multi Layer Perceptron Neural Network Multilayer Perceptron Channel Parameter Neural Network Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mutlu Avci
    • 1
  • Tulay Yildirim
    • 2
  1. 1.Computer Engineering DepartmentCukurova UniversityAdanaTurkey
  2. 2.Electronics and Communication Engineering Dept.Yildiz Technical UniversityBesiktas IstanbulTurkey

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