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Highly-efficient and scalable TrioN (3N0C) synaptic cell for analog process-in-memory.

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A novel capacitorless synaptic device (TrioN) using amorphous indium gallium zinc oxide (a-IGZO) enables efficient neuromorphic computing. This technology offers high density, fast switching, and improved accuracy for neural networks.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Electrical Engineering

Background:

  • Non-volatile memory (NVM) cross-point arrays are crucial for analog process-in-memory (aPIM) neuromorphic architectures.
  • NVM device asymmetry necessitates capacitor-based synaptic cells, increasing complexity.
  • Existing solutions face challenges in density, fabrication, and energy efficiency.

Purpose of the Study:

  • To introduce a novel, capacitorless synaptic cell for neuromorphic computing.
  • To leverage amorphous indium gallium zinc oxide (a-IGZO) for improved device performance.
  • To demonstrate a simplified fabrication process for high-density neuromorphic arrays.

Main Methods:

  • Developed a 3-NMOS 0-capacitor (TrioN, 3N0C) cross-point device using a-IGZO.
  • Fabricated hardware to demonstrate selective updates and switching characteristics.
  • Conducted neural network simulations using stochastic gradient descent (SGD) and Tiki-Taka algorithm version 1 (TTv1) on a multi-layer perceptron (MLP) for the MNIST dataset.

Main Results:

  • TrioN exhibits perfect symmetry, a high on/off ratio, and ultra-fast 10 ns switching.
  • Fabricated hardware achieved precise selective updates with 2-cycle updates, enhancing speed and energy efficiency.
  • Achieved high accuracy of 96.89% (SGD) and 97.19% (TTv1) on the MNIST dataset.

Conclusions:

  • TrioN offers a compact, energy-efficient, and scalable solution for neuromorphic computing.
  • The capacitorless design simplifies fabrication and increases array density.
  • a-IGZO-based TrioN devices show significant potential for next-generation AI hardware.