New-Generation Design-Technology Co-Optimization (DTCO): Machine-Learning Assisted Modeling Framework

被引：13

作者：

Zhang, Zhe ^{[1
]}

Wang, Runsheng ^{[1
]}

Chen, Cheng ^{[1
]}

Huang, Qianqian ^{[1
]}

Wang, Yangyuan ^{[1
]}

Hu, Cheng ^{[2
]}

Wu, Dehuang ^{[2
]}

Wang, Joddy ^{[2
]}

Huang, Ru ^{[1
]}

机构：

[1] Peking Univ, Inst Microelect, Beijing 100871, Peoples R China

[2] Synopsys Inc, Mountain View, CA 94043 USA

来源：

2019 SILICON NANOELECTRONICS WORKSHOP (SNW) | 2019年

关键词：

D O I：

10.23919/snw.2019.8782897

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we propose a machine-learning assisted modeling framework in design -technology cooptimization (DTCO) flow. Neural network (NN) based surrogate model is used as an alternative of compact model of new devices without prior knowledge of device physics to predict device and circuit electrical characteristics. This modeling framework is demonstrated and verified in FinFET with high predicted accuracy in device and circuit level. Details about the data handling and prediction results are discussed. Moreover, same framework is applied to new mechanism device tunnel FET (TFET) to predict device and circuit characteristics. This work provides new modeling method for DTCO flow.

引用

页码：17 / 18

页数：2

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