Deep Learning Automates Parameter Extraction For 2D Transistors (Stanford, SLAC)

Researchers from Stanford University and SLAC National Accelerator Laboratory published a technical paper titled “Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors.” Abstract Excerpt: “We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, and defect profiles) of two-dimensional (2D) transistors from electrical measurements, enabling... » read more