Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning With Autoencoder
Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning With Autoencoder
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DOI：
10.1109/led.2020.3045064
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发表时间：
2021-02-01
影响因子：
4.9
通讯作者：
Wong, Hiu-Yung
中科院分区：
文献类型：
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作者：
Mehta, Kashyap;Wong, Hiu-Yung
在这封信中，我们展示了使用由技术计算机辅助设计（TCAD）生成的数据训练的机器预测完整晶体管电流-电压（IV）和电容-电压（CV）曲线的可能性。以3D FinFET IDVG和CGVG预测为例。该机器是通过使用自动编码器（AE）的流形学习来构建的，以提取潜变量，然后通过三阶多项式回归将其与物理参数相关联。在机器学习过程中不需要器件物理领域的专业知识，因为不需要从TCAD训练数据中提取器件指标，例如漏极（g（m））或漏极诱导势垒降低（DIBL）。我们表明，该机器不仅可以预测完整的IV/ CV曲线，还可以预测g（m）（一阶导数量）和DIBL（从由不同数据训练的两台机器中提取）。即使使用< 50个训练数据也可以获得良好的结果。我们的工作表明，流形学习是可能的，在设备IV和CV捕捉复杂的物理，因此，它是可以预测的IV/ CV的新设备使用有限的实验数据之前，基础物理是很好地理解。
In this letter, we demonstrated the possibility of predicting full transistor current-voltage (IV) and capacitance-voltage (CV) curves using machines trained by Technology Computer-Aided Design (TCAD) generated data. 3D FinFET IDVG and CGVG predictions are used as examples. The machine is constructed through manifold learning using Autoencoder (AE) to extract the latent variables which are then correlated to physical parameters through 3rd-order polynomial regression. No device physics domain expertise is required in the machine learning process because there is no need to extract device metrics such as transconductance (g(m)) or Drain-Induced-BarrierLowering (DIBL) from the TCAD training data. We show that the machine can predict not just the full IV/ CV curves but also g(m) (1st derivative quantity) and DIBL (extracted from two machines trained by different data). Good results can be obtained even with < 50 training data. Our work shows that manifold learning is possible in device IV and CV to capture the complex physics and, thus, it is expected that it is possible to predict the IV/ CV of novel devices using limited experimental data before the underlying physics is well-understood.