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Deep Learning-Based BSIM-CMG Parameter Extraction for 10-nm FinFET.
- Source :
-
IEEE Transactions on Electron Devices . Aug2022, Vol. 69 Issue 8, p4765-4768. 4p. - Publication Year :
- 2022
-
Abstract
- 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]
- Subjects :
- *MONTE Carlo method
*DEEP learning
*THRESHOLD voltage
Subjects
Details
- Language :
- English
- ISSN :
- 00189383
- Volume :
- 69
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Electron Devices
- Publication Type :
- Academic Journal
- Accession number :
- 158517485
- Full Text :
- https://doi.org/10.1109/TED.2022.3181536