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This study demonstrates the utility of equivalent electrical circuits for modeling non-electrical systems, specifically a magnetoelectric sensor. The research optimizes sensor performance by analyzing noise and signal-to-noise ratio using circuit simulation.

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SPICEequivalent circuit modelingequivalent input noise minimizationmagnetoelectric (ME) sensorsnoise analysispiezoelectric

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Area of Science:

  • Multiphysics modeling
  • Electrical engineering
  • Sensor technology

Background:

  • Equivalent electrical circuit modeling is a valuable technique for analyzing non-electrical physical processes.
  • While finite element methods offer high accuracy, analogy-based modeling provides unique advantages for specific applications.
  • Systems integrating electronic and other physical domains benefit from circuit simulation approaches.

Purpose of the Study:

  • To demonstrate the application of equivalent electrical circuit modeling for a solid-state magnetoelectric sensor.
  • To analyze noise budgeting and optimize the signal-to-noise ratio and resolution of the sensor.
  • To study the static and dynamic behavior of the magnetoelectric sensor system.

Main Methods:

  • Development of an equivalent electrical circuit model for the magnetoelectric sensor.
  • Simulation of the circuit model using SPICE-based circuit simulators.
  • Analysis of steady-state, phasor, and transient behaviors.
  • Validation through analytical calculations and experimental data comparison.

Main Results:

  • The equivalent electrical circuit model accurately represents the magnetoelectric sensor's behavior.
  • Noise budgeting and signal-to-noise ratio optimization strategies were successfully applied.
  • The model's predictions were validated against analytical calculations and experimental measurements, showing superior results.

Conclusions:

  • Equivalent electrical circuit modeling is a powerful and effective method for analyzing complex systems like magnetoelectric sensors.
  • SPICE-based simulation enables detailed investigation of sensor performance, including noise and dynamic characteristics.
  • This approach offers a practical alternative for modeling hybrid physical systems, enhancing research and development capabilities.