A conventional Michelson interferometer is modified and used to form the various types of interferometers. The basic system consists of a conventional Michelson interferometer with silicon-graphene-gold embedded between layers on the ports. When light from the monochromatic source is input into the system via the input port (silicon waveguide), the change in optical path difference (OPD) of light traveling in the stacked layers introduces the change in the optical phase, which affects to the electron mean free path within the gold layer, induces the change in the overall electron mobility can be seen by the interferometer output visibility. Further plasmonic waves are introduced on the graphene thin film and the electron mobility occurred within the gold layer, in which the light-electron energy conversion in terms of the electron mobility can be observed, the gold layer length is 100 nm. The measurement resolution in terms of the OPD of ∼ 50 nm is achieved. In applications, the outputs of the drop port device of the modified Michelson interferometer can be arranged by the different detectors, where the polarized light outputs, the photon outputs, the electron spin outputs can be obtained by the interference fringe visibility, mobility visibility and the spin up-down splitting output energies. The modified Michelson interferometer theory and the detection schemes are given in details.
The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, and transmissions are connected via neurons. Communication signals are carried by electron spin (up and down) and adjustable Rabi frequency. Hidden variables and deep brain signals can be obtained by external detection. A Rabi antenna has been developed by simulation using computer simulation technology (CST) software. Additionally, a communication device has been developed that uses the Optiwave program with Finite-Difference Time-Domain (OptiFDTD). The output signal is plotted using the MATLAB program with the parameters of the OptiFDTD simulation results. The proposed antenna oscillates in the frequency range of 192 THz to 202 THz with a maximum gain of 22.4 dBi. The sensitivity of the sensor is calculated along with the result of electron spin and applied to form a human brain connection. Moreover, intelligent machine learning algorithms are proposed to identify high-quality transmissions and predict the behavior of transmissions in the near future. During the process, a root mean square error (RMSE) of 2.3332(±0.2338) was obtained. Finally, it can be said that our proposed model can efficiently predict human mind, thoughts, behavior as well as action/reaction, which can be greatly helpful in the diagnosis of various neuro-degenerative/psychological diseases (such as Alzheimer's, dementia, etc.) and for security purposes.