关键词:
Sensors
Magnetic fields
Magnetic sensors
Sensitivity
MOSFET
Mathematical models
Lorentz covariance
Temperature sensors
Magnetic hysteresis
Superconducting magnets
MagFET
neural networks (NNs)
rectangular MOSFET
sensitivity
sensor
摘要:
This study explores the effectiveness of nonlinear regression (LR) machine learning (ML) models and custom neural networks (NNs) for regression tasks for magnetic field sensing using a rectangular MOS transistor. We focus on sensitivity and average percentage error (APE), comparing various models under controlled conditions with a gate-to-source voltage (VGS) of 1.2 V, a drain-to-source voltage (VDS) of 1.8 V, and an applied magnetic field of 1.4 mT. The empirical model establishes a baseline sensitivity of 5.0%, but its instability poses a significant challenge to reliable sensor performance. In contrast, K-nearest neighbors (KNNs), random forest (RF), and decision tree (DT) models demonstrate stable sensitivities around 8%. Notably, custom NNs achieve the highest sensitivity, approximately 10%, with stable performance and consistently low APE values around 2%. Key performance metrics such as mean squared error (mse), mean absolute error (MAE), and latency were analyzed. The results show that custom NNs, particularly smaller architectures, offer a compelling alternative to traditional models like KNNs and DT, balancing accuracy, stability, and computational efficiency. This highlights the potential of custom NNs to enhance sensor performance in real-world applications where instability can significantly impact the accuracy and reliability of regression tasks.