A Neural Network based Fast Parameter Extraction of Compact Hot Carrier Degradation Model in FinFETs

被引：0

作者：

Shen, Cong ^{[1
]}

Li, Yu ^{[1
]}

Dai, Wu ^{[1
]}

Zhang, Xinyue ^{[1
]}

Wang, Zirui ^{[2
]}

Ji, Zhigang ^{[3
]}

Zhang, Lining ^{[1
]}

Wang, Runsheng ^{[2
]}

Huang, Ru ^{[2
]}

机构：

[1] Peking Univ, Sch Elect & Comp Engn, Beijing, Peoples R China

[2] Peking Univ, Sch Integrated Circuits, Beijing, Peoples R China

[3] SJTU, Dept Micro Nano Elect, Shanghai, Peoples R China

来源：

8TH IEEE ELECTRON DEVICES TECHNOLOGY & MANUFACTURING CONFERENCE, EDTM 2024 | 2024年

关键词：

HCD; FinFET; Parameter extraction; BSIM-CMG; NN; PSO;

D O I：

10.1109/EDTM58488.2024.10511335

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This study comprehensively investigates the parameter extraction flows for Hot Carrier Degradation (HCD) in advanced technology based on Neural Network (NN). The emerging NN-based approach and the conventional particle swarm algorithm are applied for the extraction of HCD-related parameters in the BSIM-CMG model, respectively. As verified by 16/14nm FinFET data, HCD-induced degraded characteristics can be well extracted. Taking the PSO algorithm as the baseline, the accuracy and efficiency of the NN-based approach to parameter extraction are comprehensively studied and compared. Based on the parameter extraction results and the computational costs, the emerging NN-based methods are considered to be more effective for applications in test data-intensive scenarios.

引用

页码：349 / 351

页数：3

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