Automatic I–V Parameter Extraction for GaN Devices With Image-Based Machine Learning Method
Yi Zhu (TU Delft - Electronics, Ampleon)
Marek Schmidt-Szalowski (Ampleon)
Petra Hammes (Ampleon)
Rezki Ouhachi (Ampleon)
Vittorio Cuoco (Ampleon)
Chang Gao (TU Delft - Electronics)
Qian Tao (TU Delft - ImPhys/Tao group)
John Gajadharsing (Ampleon)
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Abstract
This study presents a novel image-based machine learning (ML) method for automating I–V parameter extraction in gallium nitride (GaN) devices. Using Ampleon’s GEAR model, a dataset of 100000 simulated I–V curves are converted into I–V images through specifically designed transfer functions to train a convolutional neural network. The proposed method outperforms the existing ML method based on a fully connected neural network, particularly for I–V curves in the subthreshold region. Validation with measured pulse I–V data shows its superior accuracy, achieving a normalized mean square error (NMSE) of −30 dB compared with −24 dB with the existing ML method. The proposed method demonstrates a strong potential to accelerate the extraction and enhance the accuracy of GaN device modeling.