An Automated Machine Learning Approach to Inkjet Printed Component Analysis: A Step Toward Smart Additive Manufacturing

Autor:	Sahu, Abhishek, Aaen, Peter H., Damacharla, Praveen
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Emerging Technologies
Druh dokumentu:	Working Paper
Popis:	In this paper, we present a machine learning based architecture for microwave characterization of inkjet printed components on flexible substrates. Our proposed architecture uses several machine learning algorithms and automatically selects the best algorithm to extract the material parameters (ink conductivity and dielectric properties) from on-wafer measurements. Initially, the mutual dependence between material parameters of the inkjet printed coplanar waveguides (CPWs) and EM-simulated propagation constants is utilized to train the machine learning models. Next, these machine learning models along with measured propagation constants are used to extract the ink conductivity and dielectric properties of the test prototypes. To demonstrate the applicability of our proposed approach, we compare and contrast four heuristic based machine learning models. It is shown that eXtreme Gradient Boosted Trees Regressor (XGB) and Light Gradient Boosting (LGB) algorithms perform best for the characterization problem under study. Comment: 2024 IEEE Texas Symposium on Wireless & Micrwowave Circuits and Systems
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.04623 Zobrazit plný text záznamu View this record from Arxiv