Static Excitatory Synapse with an Integrate Fire Neuron Circuit

Artificial Intelligence (AI) and other advanced fields benefit from neuromorphic computing. As the basic building block of the nervous system, biological neurons are employed as the basis for the circuits used in neuromorphic computing. The great promise of neuromorphic devices cannot be realized with the materials now employed in computers. Something like silicon cannot control the current between artificial neurons because the physical characteristics of silicon cause the current to flow randomly throughout the device. Compared to their digital equivalents, the circuits compactness and low power consumption are guaranteed by the analogue practical deployment of metal oxide field effect transistor (MOSFET)-based neuromorphic circuits that operate in the subthreshold region. Consequently, to develop neuron circuits depending on Integrate and Fire Neuron Models (IFN) in this research is proposed. Additionally, enhancing the aforementioned 180nm CMOS VLSI-based circuitry is also observed. The circuits are simulated using LTSpice and Cadence tools. To improve the performance of the circuit, MOSFET is replaced with superior low subthreshold voltage FETs and employ related simulation software that supports such FETs.