Analog switching and retention modulation in stack-designed InGaZnO memristors for neuromorphic systems

  • Seung Joo Myoung
  • , Dong Hyeop Shin
  • , Jung Rae Cho
  • , Seungki Kim
  • , Seong Hoon Jeon
  • , Wonjung Kim
  • , Jungwoo Lee
  • , Changwook Kim
  • , Jong Ho Bae
  • , Sung Jin Choi
  • , Dong Myong Kim
  • , Sungjun Kim
  • , Dae Hwan Kim

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, three types of IGZO-based analog memristors (Mo/IGZO/Pd: S1, Mo/Al2O3/IGZO/Pd: S2, Pd/IGZO/SiO2/p+-Si: S3) were designed to exhibit distinct switching mechanisms and key electrical characteristics for synaptic device applications. A comprehensive analysis was conducted using I-V curve analysis and energy band diagrams to examine conduction mechanisms and Schottky barrier modulation. During the set and reset operations of S1 and S2, as well as the set operation of S3, electron transport over the Schottky barrier is governed by thermionic emission. However, in the reset operation of S1, incomplete VO2+ neutralization hinders barrier recovery, enabling alternative conduction paths and resulting in ohmic-like behavior. Unlike abrupt switching driven by the formation and rupture of conductive filaments (CFs), the IGZO-based memristors demonstrated gradual switching behavior via Schottky barrier (ϕB) modulation. The retention, endurance, linearity, and conductance state characteristics of each device were quantitatively evaluated. Among the three devices, S3 exhibited superior retention and endurance characteristics compared to the other devices, along with a larger number of conductance states. Furthermore, the S3 device demonstrated outstanding pattern recognition performance, achieving a high accuracy of 95.31 % when tested with the MNIST database, which is attributed to its robust retention properties. This study presents a systematic comparison of IGZO-based analog memristors fabricated under identical process conditions, highlighting how stack configuration and electrode choice affect switching mechanisms and neuromorphic performance. Notably, These results suggest that S3 could be a promising candidate for synaptic devices in neural network systems due to its analog switching characteristics, high retention, and endurance properties.

Original languageEnglish
Article number109897
JournalMaterials Science in Semiconductor Processing
Volume199
DOIs
StatePublished - 15 Nov 2025

Keywords

  • InZnGaO
  • Memristor
  • Neuromorphic system
  • Resistive switching
  • Retention

Fingerprint

Dive into the research topics of 'Analog switching and retention modulation in stack-designed InGaZnO memristors for neuromorphic systems'. Together they form a unique fingerprint.

Cite this