Prediction of Process Variation Effect for Ultrascaled GAA Vertical FET Devices Using a Machine Learning Approach

Kyul Ko, Jang Kyu Lee, Myounggon Kang, Jongwook Jeon, Hyungcheol Shin

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

In this brief, we present an accurate and efficient machine learning (ML) approach which predicts variations in key electrical parameters using process variations (PVs) from ultrascaled gate-all-around (GAA) vertical FET (VFET) devices. The 3-D stochastic TCAD simulation is the most powerful tool for analyzing PVs, but for ultrascaled devices, the computation cost is too high because this method requires simultaneous analysis of various factors. The proposed ML approach is a new method which predicts the effects of the variability sources of ultrascaled devices. It also shows the same degree of accuracy, as well as improved efficiency compared to a 3-D stochastic TCAD simulation. An artificial neural network (ANN)-based ML algorithm can make multi-input -multi-output (MIMO) predictions very effectively and uses an internal algorithm structure that is improved relative to existing techniques to capture the effects of PVs accurately. This algorithm incurs approximately 16% of the computation cost by predicting the effects of process variability sources with less than 1% error compared to a 3-D stochastic TCAD simulation.

Original languageEnglish
Article number8826316
Pages (from-to)4474-4477
Number of pages4
JournalIEEE Transactions on Electron Devices
Volume66
Issue number10
DOIs
StatePublished - Oct 2019

Keywords

  • Artificial neural network (ANN)
  • gate-all-around (GAA)
  • machine learning (ML)
  • process variation (PV)
  • vertical device

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