Optimizing Bio-Sensor Design With Support Vector Regression Technique for AlGaN/GaN MOS–HEMT

This letter introduces a novel approach using support vector regression (SVR) for sensitivity modeling of gallium nitride (GaN) metal oxide semiconductor (MOS)–high electron mobility transistors (HEMTs). By combining experimental and simulation results, the SVR-based model is developed to predict sensitivities. The fabricated AlGaN/GaN HEMTs incorporate a graded transition scheme, a 1-nm AlN spacer, 2-nm GaN cap layer, and 10-nm Al
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as the gate dielectric/sensing layer. To train the model, feature matrices are prepared using pH sensing results from 32 device dimensional variants. The trained model is then used to predict sensitivities for other device dimensions, allowing for device design optimization and exploration of the design space. Among the five considered kernels (linear, cubic, fine Gaussian, medium Gaussian, and coarse Gaussian), the quadratic-kernel-based SVR demonstrates the best performance, yielding a root mean square (RMS) error of 0.1767 and a standard deviation of 0.0654.