Design of Operational Amplifier using Artificial Neural Network

It is well known that the mathematical modeling and manual computations of the transistor design parameters are sometimes impractical and remains a challenge for researchers. In submicron technologies, the basic component of a three-stage operational amplifier such as MOSFET is modeled by various complex nonlinear equations. The modeling equations include parameters such as channel length (L), channel width (W), node voltages, and branch currents. However, the design and analysis of such complex nonlinear equations are depending on the expertise of the designer. In this paper, a Neural network is used to design and implement a three-stage operational amplifier. The channel length (L) and width (W) which are most suitable for circuit characteristics are calculated using neural network training. The Cadence tool is used to simulate the various circuit diagrams. The Neural Network model is developed and trained using MATLAB 2019b software platform. The effectiveness of the proposed neural network model is tested using the various circuits.