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

Zhe Zhang; Runsheng Wang; Cheng Chen; Qianqian Huang; Yangyuan Wang; Cheng Hu; Dehuang Wu; Joddy Wang; Ru Huang

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arXiv · 1904.10269

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

Zhe Zhang·Runsheng Wang·Cheng Chen·Qianqian Huang·Yangyuan Wang·Cheng Hu·Dehuang Wu·Joddy Wang·Ru Huang

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Abstract

In this paper, we propose a machine-learning assisted modeling framework in design-technology co-optimization (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.

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BibTeX RIS

Zhe Zhang, Runsheng Wang, Cheng Chen, Qianqian Huang, Yangyuan Wang, Cheng Hu, Dehuang Wu, Joddy Wang, Ru Huang. 2019-04-23. New-Generation Design-Technology Co-Optimization (DTCO): Machine-Learning Assisted Modeling Framework. https://arxiv.org/abs/1904.10269

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