FUSE-TCAD: A Diffusion-Based Multi-Field Surrogate for High-Fidelity TCAD Device Simulation

This publication has 17 references indexed in Scilit:

• An Efficient and Accurate DTCO Simulation Framework for Reliability and Variability-Aware Explorations of FinFETs, Nanosheets, and Beyond
  2024
• Device simulations with A U-Net model predicting physical quantities in two-dimensional landscapes
  Scientific Reports, 2023
• A New Compact MOSFET Model Based on Artificial Neural Network With Unique Data Preprocessing and Sampling Techniques
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022
• MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021
• TCAD-Augmented Machine Learning With and Without Domain Expertise
  IEEE Transactions on Electron Devices, 2021
• Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning With Autoencoder
  IEEE Electron Device Letters, 2020
• Automatic Selection of Structure Parameters of Silicon on Insulator Lateral Power Device Using Bayesian Optimization
  IEEE Electron Device Letters, 2020
• TCAD Augmented Machine Learning for Semiconductor Device Failure Troubleshooting and Reverse Engineering
  2019
• Coupled drift-diffusion (DD) and multi-subband Boltzmann transport equation (MSBTE) solver for 3D multi-gate transistors
  2013
• Silicon-on-Insulator Technology: Materials to VLSI
  2004