Ahmet Cemal Durgun

E-mail

acdurgun@metu.edu.tr

Department

Department of Electrical and Electronics Engineering

ORCID

0000-0002-4802-2271

Scopus Author ID

36625312100

Web of Science Researcher ID

AAZ-6331-2020

Causal RL Prediction of Fine-Pitch Interconnects Using Neural Networks Ünal, Hasan Said; Durgun, Ahmet Cemal (2024-01-01) In this study, we compare physics-aware neural networks for modeling fine-pitch interconnects. Results show a 5-fold reduction in test loss when imposing DC resistance through analytical equations and preserving the causal...
Tree-Based Boosting for Efficient Estimation of S-Parameters for Package Electrical Analysis Özese, Doǧanay; Baydoǧan, Mustafa Gökçe; Durgun, Ahmet Cemal; Aygün, Kemal (2024-01-01) We propose a gradient boosted tree surrogate model for S-parameter prediction in high frequency structures with limited training data. Compared to data-hungry neural networks, our approach achieves reasonable accuracy and ...
Tree-based Sequential Sampling for Efficient Designs in Package Electrical Analysis Özese, Doǧanay; Baydoǧan, Mustafa Gökçe; Durgun, Ahmet Cemal; Aygün, Kemal (2024-01-01) The use of surrogate models (SMs) has become popular in electromagnetic (EM) design and optimization. Traditional SMs, while beneficial, are often hindered by the inherent complexity and nonlinearity of EM systems, leading...
Microstrip patch antenna modeling using neural networks with knowledge-based regularization Saçın, Ekin Su; Durgun, Ahmet Cemal (2024-01-01) Neural networks (NNs) have proven a useful surrogate model for the design and optimization of high frequency structures including antennas. Black-box NNs are known to have scalability and accuracy problems as the dimension...
A Flexible Neural Network-Based Tool for Package Second Level Interconnect Modeling Karatoprak, Furkan; Saçın, Ekin Su; Özese, Doğanay; Durgun, Ahmet Cemal; Baydoğan, Mustafa Gökçe; Aygün, Kemal; Memioğlu, Tolga (2023-12-28) This paper introduces a neural network (NN)-based practical design tool for quick assessment of package second level interconnects (SLIs) at the earlier design stages. The study addresses the well-known computational cost ...
Neural Network Modeling of Antennas on Package for 5G Applications Saçin, Ekin Su; Durgun, Ahmet Cemal (2023-01-01) The design and optimization of microwave devices require rigorous electromagnetic (EM) analysis requiring excessive computational resources. To mitigate this, neural network based machine learning methods can be used to ge...
Low Loss Skip Layer Transmission Lines with Common Mode Filtering for Packages Durgun, Ahmet Cemal; Aygun, Kemal (2022-01-01) IEEETo address the increasing bandwidth demand in chip-to-chip communication, data rates of high-speed serial input/output interfaces have been aggressively increasing, which tightens the available packaging electrical bud...
Dual sided high frequency measurement of microelectronic packages Christ, Sean R.; Durgun, Ahmet Cemal; Aygun, Kemal; Hill, Michael J. (2020-10-01) © 2020 IEEE.Traditionally, validation of simulation data of microelectronic packages has been done by high frequency measurement of coplanar transmission line structures. Although this metrology has matured over the years,...
Causal and Passive Parameterization of S-Parameters Using Neural Networks Torun, Hakki Mert; Durgun, Ahmet Cemal; Aygun, Kemal; Swaminathan, Madhavan (Institute of Electrical and Electronics Engineers (IEEE), 2020-10-01) Neural networks (NNs) are widely used to create parametric models of S-parameters for various components in electronic systems. The focus of deriving these models has so far been numerical error reduction between the NN-ge...
Enforcing Causality and Passivity of Neural Network Models of Broadband S-Parameters Torun, Hakki M.; Durgun, Ahmet Cemal; Aygun, Kemal; Swaminathan, Madhavan (2019-10-01) © 2019 IEEE.This paper proposes a method to ensure that S-Parameters generated using neural network (NN) models are physically consistent and can be safely used in subsequent time-domain simulations. This is achieved by in...

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