Simulation Augmented Machine Learning for Semiconductor Physics and Defect Discovery
Relevant papers:
Albert Lu et al., "Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model," Solid-State Electronics, Volume 198, December 2022
V. Eranki et al., "Out-of-Training-Range Synthetic FinFET and Inverter Data Generation Using a Modified Generative Adversarial Network," in IEEE Electron Device Letters, vol. 43, no. 11, pp. 1810-1813, Nov. 2022
Thomas Lu et al., "Rapid MOSFET Contact Resistance Extraction from Circuit using SPICE Augmented Machine Learning without Feature Extraction," in IEEE Transactions on Electron Devices, 2021
Harsaroop Dhillon et al., "TCAD-Augmented Machine Learning with and without Domain Expertise," in IEEE Transactions on Electron Devices, 2021.
K. Mehta et al., "Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning With Autoencoder," in IEEE Electron Device Letters, 2021.
Hiu Yung Wong et al., "TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration," in IEEE Journal of the Electron Devices Society, 2020
K. Mehta et al., "Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design," in IEEE Access, 2020.
Thomas Lu et al., “Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction,” SISPAD, 2023.
Matthew Eng et al., " Automatic TCAD Model Parameter Calibration using Autoencoder," SISPAD, 2023.
N. Yee et al., “Rapid Inverse Design of GaN-on-GaN Diode with Guard Ring Termination for BV and (VFQ)-1 Co-optimization”, ISPSD, Hong Kong, 2023.
V. Eranki et al., "Comparison of Manifold Learning Algorithms for Rapid Circuit Defect Extraction in SPICE-Augmented Machine Learning," WMED, 2022.
Sophia Susan Raju et al., “Application of Noise to Avoid Overfitting in TCAD Augmented Machine Learning,” SISPAD 2020
Bankapalli Yogeswara Sarat et al., “TCAD Augmented Machine Learning for Semiconductor Device Failure Troubleshooting and Reverse Engineering”, SISPAD 2019.
Relevant papers:
Albert Lu et al., “Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model,” Solid-State Electronics, Volume 198, December 2022
V. Eranki et al., “Out-of-Training-Range Synthetic FinFET and Inverter Data Generation Using a Modified Generative Adversarial Network,” in IEEE Electron Device Letters, vol. 43, no. 11, pp. 1810-1813, Nov. 2022
Thomas Lu et al., “Rapid MOSFET Contact Resistance Extraction from Circuit using SPICE Augmented Machine Learning without Feature Extraction,” in IEEE Transactions on Electron Devices, 2021
Harsaroop Dhillon et al., “TCAD-Augmented Machine Learning with and without Domain Expertise,” in IEEE Transactions on Electron Devices, 2021.
K. Mehta et al., “Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning With Autoencoder,” in IEEE Electron Device Letters, 2021.
Hiu Yung Wong et al., “TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration,” in IEEE Journal of the Electron Devices Society, 2020
K. Mehta et al., “Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design,” in IEEE Access, 2020.
Thomas Lu et al., “Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction,” SISPAD, 2023.
Matthew Eng et al., ” Automatic TCAD Model Parameter Calibration using Autoencoder,” SISPAD, 2023.
N. Yee et al., “Rapid Inverse Design of GaN-on-GaN Diode with Guard Ring Termination for BV and (VFQ)-1 Co-optimization”, ISPSD, Hong Kong, 2023.
V. Eranki et al., “Comparison of Manifold Learning Algorithms for Rapid Circuit Defect Extraction in SPICE-Augmented Machine Learning,” WMED, 2022.
Sophia Susan Raju et al., “Application of Noise to Avoid Overfitting in TCAD Augmented Machine Learning,” SISPAD 2020
Bankapalli Yogeswara Sarat et al., “TCAD Augmented Machine Learning for Semiconductor Device Failure Troubleshooting and Reverse Engineering”, SISPAD 2019.
