@inproceedings{f62f9798afb3460999d855593731e67f,
title = "AI-Assisted Design Automation of Circular and Asymmetric Inductor in CMOS Technology",
abstract = "This paper presents a methodology of the automated inductor generator for radio-frequency (RF) integrated circuit. We propose a machine learning assisted parameterized-cell (PCELL) generation method. This method supports polygonal, circular and asymmetric inductor shapes. The proposed PCELL has been validated in 65-nm CMOS 1P9M process and analyzed in terms of inductance (L), quality factor (Q factor) and area. An artificial-neural-network (ANN) based regression model is trained to reduce the EM based inductor design cost such as ×530 less simulation time. According to the target inductor design optimization, 1 GHz operating frequency, we achieve the relative error rates of 0.63\% and 1.73\% for L and Q factor, respectively.",
keywords = "artificial-neural-network, circular, parameterized-cell, spiral inductor",
author = "Hyun, \{Jin Won\} and Dana Kim and Choi, \{Kyung Sik\} and Nam, \{Jae Won\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st International System-on-Chip Design Conference, ISOCC 2024 ; Conference date: 19-08-2024 Through 22-08-2024",
year = "2024",
doi = "10.1109/ISOCC62682.2024.10762371",
language = "English",
series = "Proceedings - International SoC Design Conference 2024, ISOCC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "183--184",
booktitle = "Proceedings - International SoC Design Conference 2024, ISOCC 2024",
}