This article investigates the inverse design of a reconfigurable multi-band patch antenna based on graphene for terahertz applications to operate in the frequency range of 2-5THz. The simulation results show that it is possible to achieve up to 8.8 dB gain, 13 frequency bands, and 360 beam steering. A deep neural network (DNN) is used to predict the antenna parameters by given inputs like desired realized gain, main lobe direction, half power beam width, and return loss in each resonance frequency. The trained DNN model predicts almost with 93% accuracy and 3% mean square error in the shortest time. The proposed antenna finds many potential applications in the THz frequency band, such as wireless telecommunications, hyperthermia treatment of breast cancer, biomedical imaging, security screening, and material identification.
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