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

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 (L
G
) for multiple devices. The created deep learning parameter extractor is trained to use I-V and L
G
data to predict the BSIM-CMG parameters. The DL parameter extractor is verified using measured device data, with L
G
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.