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.
| Author of HS Reutlingen | Schweikardt, Matthias |
|---|---|
| DOI: | https://doi.org/10.1145/3380446.3430632 |
| ISBN: | 9781450375191 |
| 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 |
| Language: | English |
| 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): |  In Copyright - Urheberrechtlich geschützt |
