Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Peng, Zhiliang, Wang, Yicheng, Yuan, Zhengwu, Wang, Xingsheng
Institution: Huazhong University of Science and Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4394782128