Applying Hodgkin-Huxley Neuron Model for Perovskite Memristor in Circuit Simulation

The convergence of memristor technology and neuromorphic computing represents a paradigm shift in the field of electronics and artificial intelligence (AI). Memristors, with their ability to remember past electrical states, offer a fundamental building block for the realization of energy-efficient and adaptive electronic systems. Meanwhile, neuromorphic computing, inspired by the brain's architecture and functionality, aims to emulate its efficiency and adaptability in electronic circuits. This paper introduces a mathematical model, the Hodgkin-Huxley model, to describe the behavior of metal halide perovskite (MHP) memristors within the framework of neuromorphic computing. By extending a set of nonlinear differential equations and implementing them in Verilog-A code, the model facilitates the simulation and analysis of perovskite memristors within electronic circuits. Through comprehensive simulations, the model successfully reproduces both volatile and nonvolatile switching behaviors observed in perovskite-based memristors, demonstrating its versatility and applicability in electronic circuit design. The simulation results demonstrate that with appropriate excitation, the output current in the memristor nonlinearly tracks the input voltage, resulting in the observation of the initial high-resistance (HRS) state and the low-resistance state (LRS) in the presented structures. Moreover, Altering the scan and range of the input voltage can subsequently modify the resistance of the memristors and the threshold voltage. The design is presented, along with its corresponding simulation results using the Cadence Virtuoso toolset. The proposed MHP memristors structure combines the density and scalability advantages of memristive crossbar arrays with the feasibility of implementing them at the top of the CMOS layer. These findings offer insights into the potential of perovskite memristors to revolutionize various fields, including artificial intelligence, energy-efficient computing architectures, and adaptive signal processing. This breakthrough promises to accelerate the development of next-generation electronic systems, bringing us closer to realizing the full potential of memristor-based technologies.