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Updated: Oct 12, 2025

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Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems.

Xiaodong Yan1, Justin H Qian1, Vinod K Sangwan1

  • 1Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA.

Advanced Materials (Deerfield Beach, Fla.)
|November 23, 2021
PubMed
Summary

Memtransistors, utilizing low-dimensional nanomaterials, offer tunable, bio-realistic functions for energy-efficient artificial intelligence (AI) neuromorphic circuits. Research surveys progress in these multi-terminal non-volatile memory (NVM) devices and their integration challenges.

Keywords:
artificial intelligencegate-tunable devicesmemristorsnon-volatile memoryvan der Waals materials

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • The brain's energy-efficient processing inspires neuromorphic circuits.
  • Existing non-volatile memory (NVM) devices have limitations for complex neural functions.
  • Memtransistors offer electrostatic control over memory and learning behaviors.

Purpose of the Study:

  • To provide a conceptual overview of memtransistors in neuromorphic circuits.
  • To survey recent advancements in memtransistors and related multi-terminal NVM devices.
  • To classify device concepts based on synaptic behavior control and tunability.

Main Methods:

  • Reviewing literature on memtransistors and multi-terminal NVM devices.
  • Classifying devices by materials, architecture, and control over synaptic behavior.
  • Highlighting nanomaterial properties like reduced dimensionality and phase-change effects.
  • Discussing wafer-scale integration strategies and material challenges.

Main Results:

  • Memtransistors and dual-gated NVM devices show promise for bio-realistic functions.
  • Nanomaterials' unique properties are key to tunable synaptic behavior.
  • Progress is shown in dual-gated floating-gate memories, ferroelectric transistors, and van der Waals heterojunctions.
  • Wafer-scale integration faces material challenges.

Conclusions:

  • Memtransistors are crucial for advanced neuromorphic circuits.
  • Harnessing nanomaterial properties is essential for device-level learning.
  • Overcoming integration and material challenges is vital for practical applications.