[paper] Compact Models for Sizing Based on ANN

Husni Habal, Dobroslav Tsonev, Matthias Schweikardt 

Compact Models for Initial MOSFET Sizing Based on Higher-order Artificial Neural Networks

ACM/IEEE Workshop on Machine Learning for CAD (MLCAD ’20)

Nov. 16–20, 2020, Virtual Event, Iceland. ACM, pp. 111-116

DOI: 10.1145/3380446.3430632

¹Infineon Technologies AG Munich, Germany
²LogiqWorks Ltd. Sofia, Bulgaria
³Reutlingen University Reutlingen, Germany

Abstract: 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 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.

Figure: ANN Model Architecture.