Work

Tree-Based Boosting for Efficient Estimation of S-Parameters for Package Electrical Analysis

Year: 2024

Type: conference-paper

Abstract: 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 trains significantly faster.

Source: IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

DOI: 10.1109/epeps61853.2024.10753981

Authors: Doğanay Özese, Mustafa Gökçe Baydoğan, Ahmet Cemal Durgun, Kemal Aygün

Institutions: Middle East Technical University, Intel (United States)

Language: English

Topic: Microwave Engineering and Waveguides

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: Semiconductor Research Corporation