Deep Learning-Based BSIM-CMG Parameter Extraction for 10-nm FinFET

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.