Real-Time and Non-Destructive Detection of Interface Trap Density Using Second Harmonic Generation with a Physics-Informed Neural Network

High Resolution Image Download MS PowerPoint Slide The interface trap density (Dit) is a critical factor influencing the performance and yield of field-effect transistors. However, obtaining Dit through conventional electrical measurements is time-consuming and hinders the optimization of the semiconductor fabrication processes. In this study, we demonstrate the potential of second harmonic generation (SHG) as a nondestructive and real-time optical method for Dit detection. By analyzing time-dependent SHG signals, we developed a comprehensive model incorporating charge density evolution and quantum tunneling effects. A simplified equation is derived and fitted to experimental data using a physics-informed neural network (PINN), revealing a strong agreement between theoretical predictions and measured results. The extracted coefficients exhibit a clear correlation with Dit, verified through a conductance–voltage analysis. To improve computational efficiency, we employ PINN with transfer learning, reducing parameter extraction time from 30 min to less than 10 s while maintaining comparable accuracy. This study highlights SHG combined with PINN as a promising alternative to conventional electrical measurements, offering a fast, noninvasive approach for semiconductor interface characterization, which can significantly enhance the efficiency of future device fabrication and optimization.