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Li-Well ZnO Memtransistors: High Reliability for Neuromorphic Applications.

Ki-Hoon Son1, Hyun-Sik Kim1, Dae-Hee Han1

  • 1Department of Materials Science & Engineering, Kyung Hee University, Yongin, 17104, Republic of Korea.

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|September 11, 2025
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Summary
This summary is machine-generated.

A novel lithium-well oxide memtransistor (LWOM) offers efficient analog memory and nonvolatile characteristics. This ionic memtransistor shows promise for next-generation artificial neural networks and memory hardware.

Keywords:
crossbar arrayhigh reliabilitylithium wellmemtransistoroxide semiconductor

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Area of Science:

  • Materials Science
  • Solid State Physics
  • Device Physics

Background:

  • Memtransistors integrate transistor functionality with nonvolatile memory using ionic memristive channels.
  • Existing memtransistor technologies like FeFETs and charge-trap flash have limitations.
  • Ionic memtransistors have historically lagged behind in performance compared to other nonvolatile memory devices.

Purpose of the Study:

  • To introduce a facile and extendable lithium-well oxide memtransistor (LWOM) as a high-performance ionic memtransistor candidate.
  • To demonstrate analog memory characteristics via Schottky barrier modulation induced by Li-ion migration.
  • To evaluate LWOM's potential for nonvolatile memory and artificial neural network (ANN) acceleration.

Main Methods:

  • Fabrication of LWOM devices using mature oxide semiconductor technology with a 230°C thermal budget.
  • Induction of Li⁺-ion migration via write VDS to modulate the Schottky barrier.
  • Characterization using 3D secondary ion mass spectrometry (SIMS) to confirm Li-ion redistribution and resistance-switching mechanism.
  • Testing of a 21 × 21 crossbar array for operational yield and weight update accuracy.

Main Results:

  • LWOM exhibits low-voltage weight updates and precise gate-controlled weight update characteristics.
  • 3D SIMS analysis confirmed Li-ion redistribution and the underlying resistance-switching mechanism.
  • A 21 × 21 crossbar array achieved a 99.31% operational yield with successful weight updates to target conductance values.

Conclusions:

  • LWOM is a promising candidate for next-generation nonvolatile memory due to its facile fabrication and performance.
  • The device's analog memory characteristics and precise control make it suitable for artificial neural network (ANN) acceleration hardware.
  • LWOM leverages mature oxide semiconductor technology, offering a viable path for scalable memory solutions.