Multi-objective optimization of CMOS low noise amplifier through nature-inspired swarm intelligence

Abstract

This paper presents the application of two swarm intelligence techniques, multi-objective artificial bee colony (MOABC) and multi-objective particle swarm optimization (MOPSO), to the optimal design of a complementary metal oxide semiconductor (CMOS) low noise amplifier (LNA) cascode with inductive source degeneration. The aim is to achieve a balanced trade-off between voltage gain and noise figure. The optimized LNA circuit operates at 2.4 GHz with a 1.8 V power supply and is implemented in a 180 nm CMOS process. Both optimization algorithms were implemented in MATLAB and evaluated using the ZDT1, ZDT2, and ZDT3 test functions. The optimized designs were then simulated using the advance design system (ADS) simulator. The results showed that the MOABC and MOPSO techniques are practical and effective in optimizing LNA design, resulting in better performance than previously published works, with a gain of 21.2 dB and a noise figure of 0.848 dB.

A multi-objective optimization based doherty power amplifier and its matching network optimization method

“…In Eq (6), Ie j represents the collection density of the population; e(p j ) stands for the distance between the jth individual and a non empty set composed of target values with a population size of I, as expressed in Eq (7).…”

“…This effectively improved the performance of the power amplifier itself [ 6 ]. Bouali H and others proposed a multi-objective particle swarm optimization algorithm applied in power amplifiers based on swarm intelligence algorithm to solve the problem that it is difficult to balance the noise and voltage gain of traditional power amplifiers, to balance the noise and voltage gain based on the design of low-noise amplifiers [ 7 ]. Purushothaman K E et al addressed the issue of power amplifiers no longer meeting practical needs in the context of fifth generation communication technology, and optimized the power consumption and energy efficiency of current power amplifiers by using multi-objective sine cosine optimization algorithms, thereby effectively improving their reliability and accuracy [ 8 ].…”

An enhanced multi-objective artificial bee colony algorithm with non-dominated sorting strategy

“…In various disciplines, such as engineering and computer science, professionals often encounter multi-objective optimization challenges that require balancing several goals simultaneously [1], [2]. These problems, rather than having a single solution, necessitate exploring a range of optimal outcomes known as a paret front.…”