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Compact models for initial MOSFET sizing based on higher-order artificial neural networks

  • 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.

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Author of HS ReutlingenSchweikardt, Matthias
Erschienen in:MLCAD '20: Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD , 16-20 November 2020, Virtual Event, Iceland
Publisher:Association for Computing Machinery
Place of publication:New York
Document Type:Conference proceeding
Publication year:2020
Tag:BSIM; MOSFET; compact model; layout; neural network; sizing
Page Number:6
First Page:111
Last Page:116
DDC classes:600 Technik
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt