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Summary

This study presents a time-efficient Matlab tool for simulating artificial neural networks in microelectronic integrated circuits. The tool ensures reliable behavioral models for sensor calibration, improving design processes.

Keywords:
Artificial Neural NetworksCMOS ASICscircuit simulationembedded systemshigh-level modeling

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Area of Science:

  • Microelectronic Engineering
  • Computational Neuroscience
  • Integrated Circuit Design

Background:

  • Current microelectronic integrated system design relies on complex simulations that are time-consuming and may compromise result reliability.
  • Increasing circuit complexity exacerbates simulation challenges, impacting design timelines and accuracy.

Purpose of the Study:

  • To introduce a high-level simulation tool for artificial neural networks (ANNs) in mixed analog-digital CMOS processes.
  • To provide a reliable, time-efficient solution for behavioral modeling of microelectronic integrated circuits for sensor calibration.

Main Methods:

  • Development of a simulation tool using Matlab for ANNs.
  • Implementation of adaptable neural model architectures within the tool.
  • Behavioral modeling of microelectronic integrated circuits for sensor calibration.

Main Results:

  • The tool enables accurate prediction of microelectronic IC system behavior under various operating conditions.
  • Demonstrated adaptability of neural model architecture to specific electronic system characteristics.
  • Achieved a simple, time-efficient, and reliable simulation solution.

Conclusions:

  • The proposed Matlab-based tool offers a significant improvement for simulating ANNs in microelectronic systems.
  • Accurate behavioral models can be generated prior to physical implementation, enhancing design reliability and efficiency.
  • The tool is particularly beneficial for sensor calibration applications in mixed-signal integrated circuits.