Modeling Emerging Technologies using Machine Learning: Challenges and Opportunities
Modeling Emerging Technologies using Machine Learning: Challenges and Opportunities
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使用机器学习对新兴技术进行建模：挑战和机遇
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
H. Amrouch
中科院分区：
文献类型：
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作者：
F. Klemme;Jannik Prinz;V. M. V. Santen;J. Henkel;H. Amrouch
晶体管的紧凑模型通过电路仿真充当半导体技术和电路设计之间的纽带。不幸的是，紧凑的模型开发和校准是一项具有挑战性和耗时的任务，阻碍了新兴技术中电路的快速原型设计（通过电路模拟）。此外，铸造厂希望保护他们的机密技术细节，以防止逆向工程。因此，他们限制了对商业技术的紧凑晶体管模型的访问（例如，有保密协议）。在这项工作中，我们提出机器学习（ML）来弥合早期设备测量和后来发生的紧凑模型开发之间的差距。我们的方法采用了一个神经网络（NN），它可以在不了解半导体物理的情况下捕获传统FinFET晶体管的电响应。此外，我们的方法可以应用于新兴技术，以负电容FinFET （NC-FinFET）为例（具有挑战性的建模）新兴技术。从本质上讲，机器学习方法的黑箱特性使技术制造细节保密。此外，我们展示了单独使用R2分数作为适应度函数是不够的，而是提出了基于关键电特性或晶体管（如阈值电压）的适应度。我们基于神经网络的晶体管建模可以推断出R2值大于0.99的FinFET和NC-FinFET，晶体管特性在实验数据的5%以内。
Compact models of transistors act as the link between semiconductor technology and circuit design via circuit simulations. Unfortunately, compact model development and calibration is a challenging and time-intensive task, hindering rapid prototyping of a circuit (via circuit simulations) in emerging technologies. Moreover, foundries want to protect their confidential technology details to prevent reverse engineering. Hence, they limit access to compact transistor models of commercial technologies (e.g., with Non-Disclosure-Agreements). In this work, we propose Machine Learning (ML) to bridge the gap between early device measurements and later occurring compact model development. Our approach employs a Neural Network (NN) that captures the electrical response of a conventional FinFET transistor without knowledge of semiconductor physics. Additionally, our approach can be applied to emerging technologies, using Negative Capacitance FinFET (NC-FinFET) as an example for a (challenging to model) emerging technology. Inherently, the black-box nature of ML approaches keeps technology manufacturing details confidential. Furthermore, we show how using solely R2 score as our fitness function is insufficient and instead propose fitness based on key electrical characteristics or transistors like threshold voltage. Our NN-based transistor modeling can infer FinFET and NC-FinFET with an R2 score larger than 0.99 and transistor characteristics within 5% of experimental data.