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Array-Integrated Memristor with an Interference-Suppressed Pulse Scheme for Multibit Neuromorphic and Edge Computingopen access

Authors
Noh, MinseoByun, YongjinKim, GimunPark, JunhyeokKim, SungjoonKim, Sungjun
Issue Date
Sep-2025
Publisher
American Chemical Society
Keywords
crossbar array; overshoot layer; synaptic behaviors; neuromorphic computing; EMNIST
Citation
ACS Applied Electronic Materials, v.7, no.17, pp 8211 - 8226
Pages
16
Indexed
SCIE
SCOPUS
Journal Title
ACS Applied Electronic Materials
Volume
7
Number
17
Start Page
8211
End Page
8226
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/59104
DOI
10.1021/acsaelm.5c01300
ISSN
2637-6113
2637-6113
Abstract
In this study, we developed a Pt/Al/TiO y /TiO x /HfO2/Pt memristor device featuring an optimized annealing process and an integrated TiO y overshoot layer to mitigate current overshoot during electroforming, achieving current-compliant-free and forming-free features. Extensive characterization demonstrated stable resistive switching properties, including a high on/off ratio (similar to 10), reliable retention, and endurance across a 24 x 24 crossbar array. Multilevel cell operation enabled precise programming, achieving up to 6-bit levels through the Incremental Step Pulse with Verify Algorithm (ISPVA) method. The device's synaptic potential was further evaluated using the Extended Modified National Institute of Standards and Technology (EMNIST) data set. ISPVA-based training achieved superior classification accuracy of 92.6% for a subset (N = 6) and 83.34% for the full alphabet (N = 26), outperforming conventional incremental pulse methods. Furthermore, resistive switching voltage range-based program sequencing makes weight transfer accurate. These findings highlight the Pt/Al/TiO y /TiO x /HfO2/Pt memristor as a core synaptic element for scalable, high-density, and energy-efficient neuromorphic computing systems.
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