  This paper presents an implementation of multilayer feed forward neural
networks (NN) to optimize CMOS analog circuits. For modeling and design
recently neural network computational modules have got acceptance as an
unorthodox and useful tool. To achieve high performance of active or passive
circuit component neural network can be trained accordingly. A well trained
neural network can produce more accurate outcome depending on its learning
capability. Neural network model can replace empirical modeling solutions
limited by range and accuracy.[2] Neural network models are easy to obtain for
new circuits or devices which can replace analytical methods. Numerical
modeling methods can also be replaced by neural network model due to their
computationally expansive behavior.[2][10][20]. The pro- posed implementation
is aimed at reducing resource requirement, without much compromise on the
speed. The NN ensures proper functioning by assigning the appropriate inputs,
weights, biases, and excitation function of the layer that is currently being
computed. The concept used is shown to be very effective in reducing resource
requirements and enhancing speed.
