Artificial Neural Network Model for Design Optimization of 2-stage Op-amp

Abstract

The existence of multi-dimensional tradeoffs among performance metrics is an impediment to the development of reliable Electronic Design Automation (EDA) tools for the design of analog circuits. To overcome this limitation, soft computing techniques that have functionality similar to the human brain are being explored. This work proposes a methodology for the development of a fast and accurate Artificial Neural Network (ANN) model for analog circuits, to achieve optimization of its design, considering a 2-stage op-amp for illustration. In order to map complex and multiple input-output relationships of analog circuits, an ANN architecture of a single hidden layer with its neuron count selected to be between the number of neurons in the input and output layers is proposed. The ANN model is developed in the MATLAB environment, and trained using SPICE-generated performance dataset. For a given set of performance metrics, the ANN model generated designs are validated against SPICE simulations. It is demonstrated that trained ANN models substitute the circuit simulator, to achieve design optimization, in a single iteration, for a new set of design specifications.