TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Habal, Husni
A1  - Tsonev, Dobroslav
A1  - Schweikardt, Matthias
T1  - Compact models for initial MOSFET sizing based on higher-order artificial neural networks
T2  - MLCAD '20: Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD , 16-20 November 2020, Virtual Event, Iceland
N2  - Simple MOSFET models intended for hand analysis are inaccurate in deep sub-micrometer process technologies and in the moderate inversion region of device operation. Accurate models, such as the Berkeley BSIM6 model, are too complex for use in hand analysis and are intended for circuit simulators. Artificial neural networks (ANNs) are efficient at capturing both linear and non-linear multivariate relationships. In this work, a straightforward modeling technique is presented using ANNs to replace the BSIM model equations. Existing open-source libraries are used to quickly build models with error rates generally below 3%. When combined with a novel approach, such as the gm/Id systematic design method, the presented models are sufficiently accurate for use in the initial sizing of analog circuit components without simulation.
KW  - MOSFET
KW  - BSIM
KW  - sizing
KW  - layout
KW  - neural network
KW  - compact model
Y1  - 2020
SN  - 9781450375191
SB  - 9781450375191
U6  - https://doi.org/10.1145/3380446.3430632
DO  - https://doi.org/10.1145/3380446.3430632
SP  - 111
EP  - 116
S1  - 6
PB  - ACM
CY  - New York
ER  -