Explainable ANN for Complementary Split Ring Resonator Design

Leonardo O. de Sousa; Mirelli S. da Silva; Gabriel G. Machado; Francisco de A. Brito Filho; Samanta M. de Holanda; Isaac B. T. da Silva

doi:10.1109/lacap67012.2026.11471224

Explainable ANN for Complementary Split Ring Resonator Design

Leonardo O. de Sousa
, Mirelli S. da Silva
, Gabriel G. Machado
, Francisco de A. Brito Filho
, Samanta M. de Holanda
, Isaac B. T. da Silva

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work presents the development of an artificial neural network (ANN) model to predict the geometric dimensions of a complementary split-ring resonator (CSRR) based on its resonance frequency and bandwidth according with a dataset generated in Ansys HFSS ®. The ANN achieved good accuracy, especially for the l dimension, with R2=0.93. Explainable AI (XAI) using SHAP was applied to interpret the model, showing a strong influence of l and s in the resonance frequency, while for bandwidth a nonlinear effect on the outputs is observed. The predicted structures were validated by electromagnetic simulations, showing maximum deviation in resonance frequency of 1.22 %. The results demonstrate the potential of combining ANN and XAI for modeling and understanding resonant microwave structures.

Original language	English
Title of host publication	2026 IEEE 2nd Latin American Conference on Antennas and Propagation (LACAP)
Publisher	IEEE
Pages	1-2
Number of pages	2
ISBN (Electronic)	979-8-3315-9779-5
ISBN (Print)	979-8-3315-9780-1
DOIs	https://doi.org/10.1109/lacap67012.2026.11471224
Publication status	Published online - 22 Feb 2026
Event	2026 IEEE 2nd Latin American Conference on Antennas and Propagation - Natal, Brazil Duration: 22 Feb 2026 → 25 Feb 2026

Conference

Conference	2026 IEEE 2nd Latin American Conference on Antennas and Propagation
Abbreviated title	LACAP
Country/Territory	Brazil
City	Natal
Period	22/02/26 → 25/02/26

Keywords

Antennas and propagation
Antennas
Filtering
Circuits
Filters
Microwave circuits
Protocols
HTTP
Communication systems
High frequency
Explainable Artificial Intelligence
Complementary Spilt Ring Resonator
SHAP
neural network

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10.1109/lacap67012.2026.11471224

Cite this

@inproceedings{56eb1e43296743f5a081f24257f67bf2,

title = "Explainable ANN for Complementary Split Ring Resonator Design",

abstract = "This work presents the development of an artificial neural network (ANN) model to predict the geometric dimensions of a complementary split-ring resonator (CSRR) based on its resonance frequency and bandwidth according with a dataset generated in Ansys HFSS {\textregistered}. The ANN achieved good accuracy, especially for the l dimension, with R2=0.93. Explainable AI (XAI) using SHAP was applied to interpret the model, showing a strong influence of l and s in the resonance frequency, while for bandwidth a nonlinear effect on the outputs is observed. The predicted structures were validated by electromagnetic simulations, showing maximum deviation in resonance frequency of 1.22 \%. The results demonstrate the potential of combining ANN and XAI for modeling and understanding resonant microwave structures.",

keywords = "Antennas and propagation, Antennas, Filtering, Circuits, Filters, Microwave circuits, Protocols, HTTP, Communication systems, High frequency, Explainable Artificial Intelligence, Complementary Spilt Ring Resonator, SHAP, neural network",

author = "\{de Sousa\}, \{Leonardo O.\} and \{da Silva\}, \{Mirelli S.\} and Machado, \{Gabriel G.\} and \{de A. Brito Filho\}, Francisco and \{de Holanda\}, \{Samanta M.\} and \{da Silva\}, \{Isaac B. T.\}",

year = "2026",

month = feb,

day = "22",

doi = "10.1109/lacap67012.2026.11471224",

language = "English",

isbn = "979-8-3315-9780-1",

pages = "1--2",

booktitle = "2026 IEEE 2nd Latin American Conference on Antennas and Propagation (LACAP)",

publisher = "IEEE",

address = "United States",

note = "2026 IEEE 2nd Latin American Conference on Antennas and Propagation , LACAP ; Conference date: 22-02-2026 Through 25-02-2026",

}

de Sousa, LO, da Silva, MS, Machado, GG, de A. Brito Filho, F, de Holanda, SM & da Silva, IBT 2026, Explainable ANN for Complementary Split Ring Resonator Design. in 2026 IEEE 2nd Latin American Conference on Antennas and Propagation (LACAP). IEEE, pp. 1-2, 2026 IEEE 2nd Latin American Conference on Antennas and Propagation , Natal, Brazil, 22/02/26. https://doi.org/10.1109/lacap67012.2026.11471224

TY - GEN

T1 - Explainable ANN for Complementary Split Ring Resonator Design

AU - de Sousa, Leonardo O.

AU - da Silva, Mirelli S.

AU - Machado, Gabriel G.

AU - de A. Brito Filho, Francisco

AU - de Holanda, Samanta M.

AU - da Silva, Isaac B. T.

PY - 2026/2/22

Y1 - 2026/2/22

N2 - This work presents the development of an artificial neural network (ANN) model to predict the geometric dimensions of a complementary split-ring resonator (CSRR) based on its resonance frequency and bandwidth according with a dataset generated in Ansys HFSS ®. The ANN achieved good accuracy, especially for the l dimension, with R2=0.93. Explainable AI (XAI) using SHAP was applied to interpret the model, showing a strong influence of l and s in the resonance frequency, while for bandwidth a nonlinear effect on the outputs is observed. The predicted structures were validated by electromagnetic simulations, showing maximum deviation in resonance frequency of 1.22 %. The results demonstrate the potential of combining ANN and XAI for modeling and understanding resonant microwave structures.

AB - This work presents the development of an artificial neural network (ANN) model to predict the geometric dimensions of a complementary split-ring resonator (CSRR) based on its resonance frequency and bandwidth according with a dataset generated in Ansys HFSS ®. The ANN achieved good accuracy, especially for the l dimension, with R2=0.93. Explainable AI (XAI) using SHAP was applied to interpret the model, showing a strong influence of l and s in the resonance frequency, while for bandwidth a nonlinear effect on the outputs is observed. The predicted structures were validated by electromagnetic simulations, showing maximum deviation in resonance frequency of 1.22 %. The results demonstrate the potential of combining ANN and XAI for modeling and understanding resonant microwave structures.

KW - Antennas and propagation

KW - Antennas

KW - Filtering

KW - Circuits

KW - Filters

KW - Microwave circuits

KW - Protocols

KW - HTTP

KW - Communication systems

KW - High frequency

KW - Explainable Artificial Intelligence

KW - Complementary Spilt Ring Resonator

KW - SHAP

KW - neural network

UR - https://pure.ulster.ac.uk/en/publications/56eb1e43-2967-43f5-a081-f24257f67bf2

U2 - 10.1109/lacap67012.2026.11471224

DO - 10.1109/lacap67012.2026.11471224

M3 - Conference contribution

SN - 979-8-3315-9780-1

SP - 1

EP - 2

BT - 2026 IEEE 2nd Latin American Conference on Antennas and Propagation (LACAP)

PB - IEEE

T2 - 2026 IEEE 2nd Latin American Conference on Antennas and Propagation

Y2 - 22 February 2026 through 25 February 2026

ER -

Explainable ANN for Complementary Split Ring Resonator Design

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