Open Access

ARTICLE

Low-Noise, High-Gain 28 GHz LNA Design Using Multi-Objective Optimization with NSGA-II and MOPSO

Spandana Saggurthi¹, Anand Nayyar², Sk Hasane Ahammad¹, Sumendra Yogarayan^3,*

1 Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, India
2 School of Computer Science and Artificial Intelligence (SCA), Duy Tan University, Da Nang, Vietnam
3 Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia

* Corresponding Author: Sumendra Yogarayan. Email:

(This article belongs to the Special Issue: Nature-Inspired Optimization & Applications in Computer Science: From Particle Swarms to Hybrid Metaheuristics)

Computers, Materials & Continua 2026, 88(3), 28 https://doi.org/10.32604/cmc.2026.080058

Received 02 February 2026; Accepted 02 April 2026; Issue published 23 July 2026

Abstract

This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA (Low noise amplifier) in 22 nm FDSOI technology using NSGA-II and MOPSO algorithms. The objectives of the paper include simultaneous minimization of noise figure (NF) and power consumption while maximizing gain under matching and stability constraints. Using device parameters and circuit models that were developed for a 22 nm FDSOI process technology, an optimization framework was created in Python, with the passive components LG, LS, LD, LOUT, and COUT chosen to be the variables optimized. The NSGA-II optimized design achieves 1.7 dB NF, 17 dB gain, and 4.7 mW DC power, while MOPSO achieves 1.8 dB NF, 17.1 dB gain, and 5.0 mW power. NSGA-II provides improved Pareto diversity and slightly better output matching, whereas MOPSO reduces computational time by 24% with comparable RF performance. The results demonstrate effective multi-objective design-space exploration and controlled algorithm benchmarking at the schematic-level for mm-wave LNA design.

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Keywords

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