Technology independent circuit sizing for fundamental analog circuits using artificial neural networks

Abstract

This study introduces technology independent neural network modeling for fundamental blocks of analog integrated circuits. The circuits modeled here are basic current mirror structures and a differential amplifier which serves as the input stage to most op-amps. Here if a designer defines the output specifications of the circuit, the neural network gives the channel widths (W) of all transistors in the circuit. It must be noted that the neural network in this novel approach is trained with the database including simulations using 1.5 mu m, 0.5 mu m, 0.35 mu m and 0.25 mu m technology SPICE parameters and the test data is constituted with simulations using only 0.18 mu m technology SPICE parameters which are not applied to the neural network for training beforehand. This shows that neural network is able to give the transistor sizes of circuit for a new unknown technology, independent on the SPICE parameters. As artificial neural network (ANN) structures, General Regression Neural Network (GRNN) and Multilayer Perceptron (MLP) having back propagation algorithm are used. Using new channel widths and lengths obtained from neural network's output, SPICE simulations of current mirrors and differential amplifier give the desired circuit output specifications for new technology.