iPREFER: An Intelligent Parameter Extractor based on Features for BSIM-CMG Models

TL;DR

This paper presents iPREFER, an automated feature extraction method using machine learning for BSIM-CMG parameter extraction, significantly improving accuracy and efficiency in device modeling and process optimization.

Contribution

The paper introduces an automated IV and CV curve feature extractor that enhances parameter extraction accuracy and efficiency for BSIM-CMG models, bridging TCAD and compact modeling.

Findings

Low fitting errors of 0.42% for CV curves and 1.28% for IV curves.

Effective adaptation to parameter variations like EOT and Gate Length.

Validated on 5-nm nanosheet devices.

Abstract

This paper introduces an innovative parameter extraction method for BSIM-CMG compact models, seamlessly integrating curve feature extraction and machine learning techniques. This method offers a promising solution for bridging the division between TCAD and compact model, significantly contributing to the Design Technology Co-Optimization (DTCO) process. The key innovation lies in the development of an automated IV and CV curve feature extractor, which not only streamlines the analysis of device IV and CV curves but also enhances the consistency and efficiency of data processing. Validation on 5-nm nanosheet devices underscores the extractor's remarkable precision, with impressively low fitting errors of 0.42% for CV curves and 1.28% for IV curves. Furthermore, its adaptability to parameter variations, including those in Equivalent Oxide Thickness and Gate Length, solidifies its potential to revolutionize the TCAD-to-compact model transition. This universal BSIM-CMG model parameter extractor promises to improve the DTCO process, offering efficient process optimization and accurate simulations for semiconductor device performance prediction.