I-V Global Parameter Extraction for Industry Standard FinFET Compact Model using Deep Learning

Fredo Chavez*, Jen Hao Chen, Chien Ting Tung, Chenming Hu, Sourabh Khandelwal

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

An I-V global parameter extraction technique for the industry standard FinFET compact model BSIM-CMG using deep learning (DL) is presented in this paper. The training data of 750k is generated by Monte Carlo simulation of key BSIM-CMG Parameters and gate length (LG) for multiple devices. The created deep learning parameter extractor is trained to use I-V and LG data to predict the BSIM-CMG parameters. The DL parameter extractor is verified using measured device data, with LG ranging from 50n to 970nm. The created global model was able to create an accurate fitting for the input characteristics of multiple devices while capturing the trends in key electrical parameters. The results show the tremendous potential of using DL to create accurate global models instantly where measurement and manufacturing errors are present.

原文English
主出版物標題2023 IEEE International Symposium on Radio-Frequency Integration Technology, RFIT 2023
發行者Institute of Electrical and Electronics Engineers Inc.
頁面20-22
頁數3
ISBN(電子)9798350324402
DOIs
出版狀態Published - 2023
事件2023 IEEE International Symposium on Radio-Frequency Integration Technology, RFIT 2023 - Cairns, Australia
持續時間: 14 8月 202316 8月 2023

出版系列

名字2023 IEEE International Symposium on Radio-Frequency Integration Technology, RFIT 2023

Conference

Conference2023 IEEE International Symposium on Radio-Frequency Integration Technology, RFIT 2023
國家/地區Australia
城市Cairns
期間14/08/2316/08/23

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