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论文类型：期刊论文

第一作者：李睿涵

通讯作者：王兴晟

合写作者：罗浩文,王奕琛,袁正午,Asen Asenov,缪向水

发表刊物：Semiconductor Science and Technology

收录刊物：SCI、EI

所属单位：华中科技大学

学科门类：工学

一级学科：电子科学与技术

文献类型：J

卷号：37

期号：9

页面范围：095010

ISSN号：0268-1242

关键字：5 nm nanosheet transistor, compact model extraction, statistical variability, artificial neural network, SRAM

DOI码：10.1088/1361-6641/ac836d

发表时间：2022-08-02

影响因子：2.66

摘要：In this paper, we look at how artificial neural networks (ANNs) may be used to improve compact model extraction of statistical variability in 5-nm nanosheet transistors (NSTs) and how it can be applied to 6NST-SRAM simulations. To begin, both the TCAD simulation platform and compact model of 3D n-type and p-type NST have been rigorously validated against the experimental data. The transfer characteristics curves of 1104 NST samples generated by metal gate granularity (MGG), random discrete dopants (RDD) and line edge roughness (LER) are used to extract the important figures of merit (FoM) including ON-current (ION), OFF-current (IOFF), threshold voltage (VTH) and subthreshold slope (SS). Meanwhile, we can collect the main compact model parameters of these NST samples using our automatic extraction technique. Furthermore, a multi-layer artificial neural network (ANN) engine is trained to anticipate the important compact model parameters by entering FoMs, which significantly speeds up the automatic extraction. When we compare the prediction results to the genuine values, we discover that their correlation coefficients are all larger than 0.99. Finally, we simulated the 6NST-SRAM circuit and obtained its stability variation, with the help of extracted NST variability by the aforementioned speedup techniques.