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Area of Science:

  • Materials Science
  • Computer Science
  • Electronics Engineering

Background:

  • Artificial neural networks (ANNs) are increasingly integrated into power-efficient thin-film electronics.
  • Thin-film technologies demand robust, manufacturable devices with efficient layouts.
  • The rectified linear unit (ReLU) is a key activation function in convolutional ANNs (CNNs).

Purpose of the Study:

  • To demonstrate that multimodal transistors (MMTs) can emulate the ReLU activation function.
  • To assess the impact of MMT transfer characteristics on CNN classification performance.
  • To explore the feasibility of using MMTs in practical, power-efficient electronic systems.

Main Methods:

  • Simulated and measured transfer characteristics of MMTs were analyzed.
  • The linear dependence in MMT saturation was identified as mimicking the ReLU function.
  • MATLAB was used to evaluate CNN performance with distorted ReLU functions and MMT proxies.

Main Results:

  • MMT transfer characteristics effectively replicate the ReLU activation function.
  • High CNN classification accuracy was maintained even with significant variations in MMT parameters.
  • The study confirmed that CNNs can utilize MMTs as activation functions due to consistent training and classification behavior.

Conclusions:

  • MMTs offer a viable pathway for implementing sophisticated, power-efficient ANNs in thin-film electronics.
  • The inherent robustness of MMTs to parameter variations supports their use in real-world applications.
  • This research bridges the gap between advanced electronic device physics and artificial intelligence implementation.