Machine Learning-Based Statistical Approach to Analyze Process Dependencies on Threshold Voltage in Recessed Gate AlGaN/GaN MIS-HEMTs

In this work, we demonstrate the use of a machine learning (ML)-based statistical approach to model and analyze the impact of the fabrication processes on the threshold voltage in recessed gate AlGaN/GaN metal-insulator-semiconductor high electron mobility transistors. First, we employ a ML-based Tikhonov regularization approach using the input of 19 different processing splits to generate a multivariable analytical equation that considers four critical processing parameters, such as the remaining AlGaN depth, ex-situ wet clean, in-situ plasma, and gate dielectric. Furthermore, the artificial neural network-based approach, which cannot be used to further analyze the impact of the process, is implemented for the comparison. Second, the results from this analytical equation show a nice agreement with the measured threshold voltage, indicating a successful correlation between the processing parameters and the threshold voltage. Finally, the impact of each process on the threshold voltage can be analyzed through the coefficient value related to each process, which can be a useful guidance for the device optimization toward the targeted performance.