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

<p>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: &#8220;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... <a class="read_more" href="https://semiengineering.com/deep-learning-automates-parameter-extraction-for-2d-transistors-stanford-slac/">&#187; read more</a></p> <p>The post <a href="https://semiengineering.com/deep-learning-automates-parameter-extraction-for-2d-transistors-stanford-slac/">Deep Learning Automates Parameter Extraction For 2D Transistors (Stanford, SLAC)</a> appeared first on <a href="https://semiengineering.com">Semiconductor Engineering</a>.</p>