Neural Network Assisted MOSFETs Gate Dielectric Traps Extraction

Neural network assisted MOSFETs gate dielectric traps extraction method as an emerging fast data-driven approach is studied. The proposed method extracts trap spatial and energetic distribution from the noise power spectral density (PSD) with two steps: 1) rough extraction using a customized multi-scale receptive fields Convolutional Neural Network (CNN), 2) refinement using Backpropagation Optimization (BO) network based on Gradient Descent (GD). The method is applied to a practical case, and successfully extracts the trap distribution.