Swarm intelligence-based mono and multi-objective methods for sizing preamplifier circuits for biomedical applications

Abstract

In this article, various high-performance optimisation algorithms are used to optimise circuit parameters: firstly in a modified double ended differential amplifier for area optimisation; and secondly in an adaptive recycling folded cascode amplifier to deal with noise power trade-off. These circuits are used as preamplifiers in neural ailment such as epileptic seizure recording systems. Amongst the various analogue design challenges in a preamplifier, minimum area consumption and a good noise power trade-off are the necessary pre-requisites which require consideration. Traditional circuit sizing methods are tedious and prone to human error, so an alternative method is necessary to auto-size the circuit parameters. In this work, single objective algorithms are implemented to minimise area in the circuit, where the whale optimisation algorithm convergences earliest. While the multi-verse multi-objective optimisation algorithm shows good performance to maintain the noise power trade-off. These results are evaluated and compared in MATLAB and Cadence environment.