Device-Simulation-Based Machine Learning Technique for the Characteristic of Line Tunnel Field-Effect Transistors

Chandni Akbar, Yiming Li*, Narasimhulu Thoti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

With the rapid growth of the semiconductor manufacturing industry, it has been evident that device simulation has been considered a sluggish process. Therefore, due to downscaling of semiconductor devices, it is significantly expensive to obtain the inevitable device simulation data because it requires complex analysis of various factors. To develop a competent technique to analyze the performance of the line tunnel field-effect transistors (TFETs), the 3-D stochastic device simulation is integrated with a machine learning (ML) algorithm, named random forest regressor (RFR). Despite producing tremendous researches by the RFR model in the field of computer vision, the adoption of these ML algorithms in the field of the semiconductor industry has a lot of margin for progress. The ML-based RFR model is exploited to predict the effect of variability sources of line TFET under different biasing conditions. Results are promising and reducing the computational cost of device simulation by 99%. The prediction of effect of source variation is less than 1% as compared to the device simulation of line TFET. The application of the RFR on the line TFET device exhibits the power and flexibility of this approach because its evaluation with different bias conditions shows outstanding results.

Original languageEnglish
Pages (from-to)53098-53107
Number of pages10
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Keywords

  • Artificial intelligence
  • intelligent manufacturing
  • line tunnel field-effect transistors
  • machine learning
  • random forest regressor

Fingerprint

Dive into the research topics of 'Device-Simulation-Based Machine Learning Technique for the Characteristic of Line Tunnel Field-Effect Transistors'. Together they form a unique fingerprint.

Cite this