Artificial Neural Network-Assisted Optimization of DM JLNC-FinFET Biosensor With Raised Source/Drain Architecture

This study presents an artificial neural network (ANN)-assisted optimization framework for the raised source-drain (RSD) dielectric-modulated (DM) junctionless (JL) Negative Capacitance (NC) FinFET biosensor, designed to detect both charged and neutral biomolecules. The biosensor features a “T-shaped” metal support, providing adsorption regions on both sides for enhanced biomolecule detection. To evaluate the ANN model’s performance, Root Mean Square Error (RMSE) and R2-score are analyzed. The network architecture comprises an input, followed by two hidden layers with 32 and 16 neurons, and an output layer with a single neuron. The model achieved a maximum R2-score of 0.9872 and a minimum RMSE of 0.0274 when trained on 90% of the dataset. Its reliability is further ensured through K-fold cross-validation. The training and prediction processes utilize a TCAD-integrated ANN framework, where the dataset is generated by systematically varying key device parameters such as channel length (Lg), channel doping concentration (NCH), fin thickness (TFin), and support work function (SMWF). A total of 1140 device simulations were performed, demonstrating the ANN capability to accurately predict the biosensor’s sensitivity to pyridine biomolecules with high precision.