Nguyen Michael Do

Electrical engineering

Hometown: Gilbert, Arizona, United States

Graduation date: Fall 2025

Additional details: First-generation college student, Honors student

FURI Semiconductor Research theme icon

FURI | Fall 2024

TCAD Simulations Aided by Physics-Informed Neural Networks

Technology-Computer-Aided-Design (TCAD) solvers are highly accurate, yet computationally expensive tools to simulate electronics device behavior. Photoconductive Semiconductor Switches (PCSS) are optoelectronic devices used in high-frequency amplification and power applications. This research project aims to compare runtime and accuracy of the TCAD solvers against the physics-informed neural network (PINN) framework by fitting PCSS device simulation data into the neural network. By implementing a PINN, this research aims to improve the efficiency of computational resources and runtime because of TCAD’s complex and tedious nature.

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