Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning with Autoencoder
IEEE Electron Device Letters
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 ID VG and CG VG 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 ( gm ) or Drain-Induced-Barrier-Lowering (DIBL) from the TCAD training data. We show that the machine can predict not just the full IV/CV curves but also gm ( 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.
Autoencoder, FinFET, machine learning, simulation, technology computer-aided design (TCAD)
Kashyap Mehta and Hiu Yung Wong. "Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning with Autoencoder" IEEE Electron Device Letters (2021): 136-139. https://doi.org/10.1109/LED.2020.3045064