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

Authors
Myoung, Seung JooShin, Dong HyeopCho, Jung RaeKim, SeungkiJeon, Seong HoonKim, WonjungLee, JungwooKim, ChangwookBae, Jong-HoChoi, Sung-JinKim, Dong MyongKim, SungjunKim, Dae Hwan
Issue Date
Nov-2025
Publisher
Elsevier Ltd
Keywords
InZnGaO; Memristor; Neuromorphic system; Resistive switching; Retention
Citation
Materials Science in Semiconductor Processing, v.199, pp 1 - 13
Pages
13
Indexed
SCIE
SCOPUS
Journal Title
Materials Science in Semiconductor Processing
Volume
199
Start Page
1
End Page
13
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/58890
DOI
10.1016/j.mssp.2025.109897
ISSN
1369-8001
1873-4081
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. © 2025 Elsevier Ltd
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