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A new deep learning (DL)-based parameter extraction method is presented in this brief; 50k training cases are generated by Monte Carlo simulations of these preselected parameters in Berkeley short-channel IGFET model (BSIM)-common multigate (CMG). DL models are trained using backward propagation with ${C} _{\text {gg}} - {V} _{g}$ and ${I} _{d} - {V} _{g}$ as the input and selected BSIM-CMG parameters as the output. A TCAD simulated FinFET device, calibrated to Intel 10-nm node, is used to test the DL models. The DL-based parameters extraction results show an excellent fit to capacitance and drain current data, with 0.16% rms error in ${C} _{\text {gg}} - {V} _{g}$ and 6.1% rms error in ${I} _{d} - {V} _{g}$ (0.69% rms error in above-threshold-voltage ${I} _{d} - {V} _{g}$), respectively. In addition, devices with a 10% variation in gate length and oxide thickness are successfully modeled with the trained DL model. The results show tremendous promise in using the DL-based models for parameter extraction. [ABSTRACT FROM AUTHOR]