iPREFER: An Intelligent Parameter Extractor based on Features for BSIM-CMG Models
arxiv(2024)
摘要
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
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.
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